2407.12861v2

# CiteME: Can Language Models Accurately Cite Scientific Claims? **Authors**: Ori Press, Andreas Hochlehnert, Ameya Prabhu, Vishaal Udandarao, Ofir Press, Matthias Bethge > Tübingen AI Center, University of TübingenOpen-𝚿𝚿\mathbf{\Psi}bold_Ψ(Open-Sci) Collective > Tübingen AI Center, University of TübingenUniversity of CambridgeOpen-𝚿𝚿\mathbf{\Psi}bold_Ψ(Open-Sci) Collective > Princeton Language and Intelligence, Princeton University \quotingsetup vskip=-.0leftmargin=0.025rightmargin=0.075font=small,itshape Abstract Thousands of new scientific papers are published each month. Such information overload complicates researcher efforts to stay current with the state-of-the-art as well as to verify and correctly attribute claims. We pose the following research question: Given a text excerpt referencing a paper, could an LM act as a research assistant to correctly identify the referenced paper? We advance efforts to answer this question by building a benchmark that evaluates the abilities of LMs in citation attribution. Our benchmark, CiteME, consists of text excerpts from recent machine learning papers, each referencing a single other paper. CiteME use reveals a large gap between frontier LMs and human performance, with LMs achieving only 4.2-18.5% accuracy and humans 69.7%. We close this gap by introducing CiteAgent, an autonomous system built on the GPT-4o LM that can also search and read papers, which achieves an accuracy of 35.3% on CiteME. Overall, CiteME serves as a challenging testbed for open-ended claim attribution, driving the research community towards a future where any claim made by an LM can be automatically verified and discarded if found to be incorrect. 1 Introduction <details> <summary>extracted/5974968/figures/fig1.png Details</summary>  ### Visual Description \n ## Diagram: Automated Citation Finding Process ### Overview This diagram illustrates a process where a text containing a citation is analyzed, and an automated search is performed to identify the cited paper. The diagram depicts a flow from input text to identified paper. ### Components/Axes The diagram consists of three main components arranged horizontally: 1. **Input Text Block:** A light blue rounded rectangle containing the text to be analyzed. 2. **Robot/Search Process:** A central illustration of a robot sitting at a computer, representing the automated search process. An arrow indicates the flow of information from the text block to the robot. 3. **Output Text Block:** A light blue rounded rectangle displaying the identified paper. An arrow indicates the flow of information from the robot to the output. ### Content Details **Input Text Block:** The text within the first block reads: "ESIM is another high performing model for sentence-pair classification tasks, particularly when used with ELMo embeddings [CITATION]". The word "[CITATION]" is enclosed in square brackets. **Robot/Search Process:** The robot is depicted as a simplified, cartoon-like figure seated at a computer. The robot has a screen displaying a generic computer interface. **Output Text Block:** The text within the third block reads: "After searching, I think the cited paper is: “Deep contextualized word representations”". The paper title is enclosed in double quotes. ### Key Observations The diagram visually represents a simple information retrieval process. The flow is unidirectional, from text input to citation identification. The use of a robot suggests automation. The brackets around "[CITATION]" in the input text indicate that this is the part of the text that triggers the search. ### Interpretation The diagram demonstrates a system for automatically identifying cited papers from text. The process begins with a text containing a citation marker (e.g., "[CITATION]"). This marker triggers a search, presumably using the surrounding text as a query. The search results in the identification of the cited paper, which is then presented as output. The phrase "I think" in the output suggests that the system may not always be certain of the correct citation, indicating a potential for error or ambiguity. The diagram highlights the potential for automation in literature review and citation management. The system appears to rely on keyword-based search, as evidenced by the use of "ELMo embeddings" in the input text and "Deep contextualized word representations" in the output. </details> Figure 1: Example of a CiteME instance. The input (left) is an excerpt from a published paper with an anonymized citation; the target answer (right) is the title of the cited paper. */ $\ddagger$ shared first/last authorship Code: github.com/bethgelab/CiteME, Dataset: huggingface.co/datasets/bethgelab/CiteME Correspondence to {ori.press, andreas.hochlehnert}@bethgelab.org Scientific discoveries are advancing at an ever-growing rate, with tens of thousands of new papers added just to arXiv every month [4]. This rapid progress has led to information overload within communities, making it nearly impossible for scientists to read all relevant papers. However, it remains a critical scholarship responsibility to check new claims and attribute credit to prior work accurately. Language models (LMs) have shown impressive abilities as assistants across tasks [25], which leads us to explore the following task in this paper: Can language models act as research assistants to help scientists deal with information overload? We make progress towards answering this question by evaluating the abilities of LMs in citation attribution [27, 59]. Given a text excerpt referencing a scientific claim, citation attribution is the task in which a system is asked to fetch the title of a referenced paper, as illustrated in Figure 1. Current benchmarks are collected automatically, which leads to the dominance of ambiguous or unattributable text excerpts that make overly broad claims or are not used as evidence for any specific claim, as shown in Table 1. Furthermore, these benchmarks typically frame citation attribution as a retrieval task from a small set of pre-selected papers where only paper titles and abstracts can be viewed, not the full paper’s content important for citation attribution [22, 50]. Table 1: Percentage of reasonable, ambiguous, unattributable, and trivial excerpts across 4 citation datasets, as labeled by human experts. For a detailed breakdown of every analyzed sample, see Appendix A. | FullTextPeerRead [42] ACL-200 [9, 58] RefSeer [40, 58] | 24 26 24 | 26 42 28 | 34 18 32 | 16 14 16 | | --- | --- | --- | --- | --- | | arXiv [33] | 10 | 50 | 30 | 10 | | Average | 21 | 36.5 | 28.5 | 14 | To address these issues, we introduce CiteME (Citation for Model Evaluation), the first manually curated citation attribution benchmark with text excerpts that unambiguously reference a single paper. CiteMe’s use of only unambiguous text excerpts eliminates the subjectivity that characterizes other benchmarks. To evaluate CiteMe, we conduct benchmark tests that focus on open-ended citation attribution. Human evaluators confirm the lack of ambiguity, achieving 69.7% accuracy while taking just 38.2 seconds on average to find the referenced papers. The current state-of-the-art system, SPECTER2 [77], experiences 0% accuracy on CiteME, highlighting the real-world difficulties of LM-based citation attribution. Similarly, current frontier LMs achieve performance of 4.2-18.5%, substantially beneath human performance. We conclude that current LMs cannot reliably link scientific claims to their sources. To bridge this gap, we introduce CiteAgent, an autonomous system built on top of the GPT-4o [1] LM and the Semantic Scholar search engine [46]. CiteAgent can search for and read papers repeatedly until it finds the referenced paper, mirroring how scientists perform this scholarship task to find targeted papers. CiteAgent correctly finds the right paper 35.3% of the time when evaluated on CiteME. In summary, our main contributions are: - CiteME, a challenging and human-curated benchmark of recent machine learning publications that evaluates the abilities of LMs to correctly attribute scientific claims. CiteME is both natural and challenging, even for SoTA LMs. - CiteAgent, an LM-based agent that uses the Internet to attribute scientific claims. Our agent uses an existing LM without requiring additional training. It also uses a search engine, which makes it applicable to real-world settings and differentiates it from systems that can search only within a predetermined corpus of papers. Future work that improves the accuracy of CiteME may lead to systems that can verify all claims an LM makes, not just those in the ML research domain. This could reduce the hallucination rate [92] and increase factuality [6] of LM-generated text. 2 The CiteME Benchmark We now present the CiteME benchmark, which we differentiate from other citation prediction benchmarks that are automatically curated, i.e., curated without human supervision or feedback in selecting text excerpts [32, 31, 9, 40, 72, 44, 42, 33]. For comparison, we study the quality of excerpts across four popular citation prediction benchmarks (FullTextPeerRead, [42], ACL-200 [9, 58], RefSeer [40, 58], and arXiv [33]). Specifically, we sample 50 excerpts from each dataset and categorize them using the following criteria: (1) Attributable vs Unattributable. The cited paper should provide evidence for the statement in the text excerpt, i.e., be an attribution as opposed to a statement that does not clearly refer to supporting evidence. Excerpts that do not follow this criterion are termed unattributable, as in the example: {quoting} For all of our experiments, we use the hyperparameters from [CITATION]. (2) Unambiguous vs Ambiguous. The cited text excerpt should not be overly broad. The ground truth cited papers should clearly be the only possible reference for the claim in the text excerpt. Excerpts that do not follow this criterion are termed ambiguous, as in the example: {quoting} [CITATION1, CITATION2] explored paper recommendation using deep networks. (3) Non-Trivial vs Trivial. The text excerpt should not include author names or title acronyms, which simply tests LM memorization and retrieval. Excerpts that do not follow this criterion are termed trivial, as in the example: {quoting} SciBERT [CITATION] is a BERT-model pretrained on scientific texts. (4) Reasonable vs Unreasonable. The text excerpt should be attributable, unambiguous and non-trivial. We term excerpts that do not follow this criterion unreasonable, but we categorize them according to the underlying issue (e.g., unattributable, ambiguous, or trivial). An example of a reasonable excerpt is: {quoting} We use the ICLR 2018–2022 database assembled by [CITATION], which includes 10,297 papers. In Table 1 (left), we demonstrate that most samples from all four datasets lack sufficient information for humans to identify the cited paper and are often labeled as ambiguous or unattributable. Additionally, an average of 17.5% of the samples are tagged as trivial because they include the title of the paper or its authors directly in the excerpt. Excerpts also frequently have formatting errors, making some nearly unreadable (see examples in Appendix A). Past work also notes similar artifacts [33, 42, 58], further supporting our claims. This analysis leads us to contend that performance on existing citation benchmarks might not reflect real-world performance of LM research assistants. In response to these deficiencies, we created CiteME, a new benchmark with human expert curation for unambiguous citation references. CiteME contains carefully selected text excerpts, each containing a single, clear citation to ensure easy and accurate evaluation. Curation. A team of 4 machine learning graduate students, henceforth referred to as “experts”, were responsible for collecting text excerpts. The experts were instructed to find samples that (1) referenced a single paper and (2) provided sufficient context to find the cited paper with scant background knowledge. Each sample was checked for reasonableness; only those deemed reasonable by two or more experts were retained. Some excerpts were slightly modified to make them reasonable. <details> <summary>extracted/5974968/figures/paper_tags.png Details</summary>  ### Visual Description \n ## Horizontal Bar Chart: CiteME Paper Tags ### Overview The image presents a horizontal bar chart displaying the frequency of various tags associated with papers on CiteME. The chart visualizes the distribution of tags, allowing for a quick comparison of their prevalence. ### Components/Axes * **Title:** "CiteME Paper Tags" - positioned at the top-center of the image. * **X-axis:** "Tag Frequency" - ranging from approximately 0 to 6, with markers at 2, 4, and 6. * **Y-axis:** Lists the following tags (from top to bottom): * Image Classification * Adversarial Machine Learning * Deep Learning Architectures * Vision-Language Models * Contrastive Learning * Multi-modal Learning * Representation Learning * Image Processing * Machine Learning Efficiency * Machine Learning Evaluation * **Bars:** Each tag is represented by a horizontal bar, with the length of the bar corresponding to its frequency. The bars are a light grey color. ### Detailed Analysis The chart displays the frequency of each tag. The trend is to read the length of each bar to determine the frequency. * **Image Classification:** Approximately 5.6 * **Adversarial Machine Learning:** Approximately 5.2 * **Deep Learning Architectures:** Approximately 4.8 * **Vision-Language Models:** Approximately 4.6 * **Contrastive Learning:** Approximately 4.4 * **Multi-modal Learning:** Approximately 4.2 * **Representation Learning:** Approximately 3.8 * **Image Processing:** Approximately 3.4 * **Machine Learning Efficiency:** Approximately 3.2 * **Machine Learning Evaluation:** Approximately 5.0 ### Key Observations * "Image Classification" and "Adversarial Machine Learning" have the highest tag frequencies, both around 5.6 and 5.2 respectively. * "Machine Learning Efficiency" has the lowest tag frequency, at approximately 3.2. * The tags are relatively evenly distributed across the frequency range, with most falling between 3 and 5. ### Interpretation The chart suggests that papers on CiteME frequently cover topics related to Image Classification, Adversarial Machine Learning, and Deep Learning Architectures. This indicates a strong focus on these areas within the research represented on the platform. The lower frequency of "Machine Learning Efficiency" might suggest that optimization and efficiency are less central themes in the papers, or that these aspects are discussed under different tags. The data provides insight into the key research areas within the CiteME dataset, which could be useful for researchers seeking relevant papers or for understanding the current trends in the field. The relatively close frequencies of many tags suggest a diverse range of research interests, rather than a single dominant theme. </details> <details> <summary>extracted/5974968/figures/citeme_hist.png Details</summary>  ### Visual Description \n ## Bar Chart: CiteME Papers by Year Published ### Overview This image presents a bar chart illustrating the percentage of CiteME papers published each year from before 2011 ('Pre') to 2024. The chart uses a light blue color for all bars. The y-axis represents the percentage of papers, while the x-axis represents the year of publication. ### Components/Axes * **Title:** "CiteME Papers by Year Published" (positioned at the top-center) * **X-axis Label:** Year (labeled with years 'Pre', '11', '12', '13', '14', '15', '16', '17', '18', '19', '20', '21', '22', '23', '24') * **Y-axis Label:** "Percent of Papers" (positioned vertically on the left side) * **Y-axis Scale:** Ranges from 0 to 25, with increments of 5. * **Legend:** No legend is present, as only one data series is displayed. ### Detailed Analysis The chart displays the following approximate data points: * **Pre '11:** Approximately 20% * **'11:** Approximately 3% * **'12:** Approximately 3% * **'13:** Approximately 3% * **'14:** Approximately 2% * **'15:** Approximately 4% * **'16:** Approximately 5% * **'17:** Approximately 5% * **'18:** Approximately 6% * **'19:** Approximately 11% * **'20:** Approximately 3% * **'21:** Approximately 16% * **'22:** Approximately 23% * **'23:** Approximately 24% * **'24:** Approximately 2% **Trends:** * The percentage of papers published was high before 2011 (approximately 20%). * There was a significant drop in publications from 2011 to 2014, with percentages hovering around 2-3%. * Publications gradually increased from 2015 to 2019, reaching approximately 11%. * There was a sharp decline in 2020 (approximately 3%). * A substantial increase in publications occurred from 2021 to 2023, peaking at approximately 24% in 2023. * A significant drop in 2024 to approximately 2%. ### Key Observations * The most significant peak in publications occurred in 2023. * The lowest publication rates were observed in 2020 and the years 2011-2014. * The data suggests a cyclical pattern, with periods of growth followed by declines. * The 2024 data point is a notable outlier, showing a dramatic decrease compared to the previous years. ### Interpretation The chart demonstrates the publication trend of CiteME papers over time. The initial high publication rate before 2011 suggests a period of foundational work or initial project activity. The subsequent decline could indicate a stabilization phase or a shift in research focus. The resurgence in publications from 2021 onwards suggests renewed interest or significant progress in the field. The sharp drop in 2024 is a significant anomaly that warrants further investigation. It could be due to various factors, such as changes in research funding, project completion, or data collection delays. The cyclical pattern suggests that CiteME research may be subject to periodic bursts of activity followed by periods of consolidation. The data highlights the importance of considering the temporal context when analyzing research output. </details> Figure 2: (Left) The top 10 most frequent labels of papers in CiteME, as identified by GPT-4. Overly broad tags like "Machine Learning" or "Deep Networks" were excluded (see Appendix D for details). (Right) Most excerpts in CiteME are from recent papers. Filtering Out the Easy Instances. To ensure that CiteMe is a challenging and robust dataset, we remove all dataset instances that GPT-4o can correctly answer. Filtering datasets by removing the samples that a strong model can correctly answer was previously done in Bamboogle [71] and the Graduate-Level Google-Proof Q&A Benchmark [73]. In our filtering process, GPT-4o was used with no Internet access or any other external tools. Therefore, it could answer only correctly specified papers that it memorized from its training process. We ran each sample through GPT-4o five times to cover its different outcomes. In the end, we filtered out 124 samples, leaving 130 samples in total. Human Evaluation. To ensure that our benchmark instances are not unsolvable, we evaluate human performance on them. Using a random subset of 100 samples, we asked a group of 20 experts, who were not part of benchmark construction, to perform the task of finding the referenced papers given only the excerpt, with each expert given 5 random samples from CiteME and a maximum of two minutes to solve each instance (similar to [47]). We observe that the experts found the correct citation 69.7% of the time, spending an average of only 38.2 seconds to do so. Note that this accuracy number does not represent the maximum-possible human performance since our annotators were limited to two minutes per question for budget reasons. Human accuracy may rise even higher given more time per instance. To check the experts’ consistency, five more experts were asked to solve the same instances previously answered by the original experts. In 71% of the cases, both experts agreed on the answer, and at least one expert got to the right answer in 93% of cases. Are 130 questions sufficient to evaluate LMs? Though traditional machine learning benchmarks usually contain thousands or even millions of test samples, recent work [17, 71, 74, 86] shows that LM benchmarks can include only 100-200 samples and remain insightful. HumanEval [17], for example, which consists of 164 programming problems, is among the most influential LM datasets today, appearing in virtually every SoTA LM paper recently published [66, 1, 81, 19]. Similarly, Bamboogle [71] contains 125 questions, DrawBench [74] contains 200 instances, and Plot2Code [86] contains 132 questions. This is in line with [70, 69], who show that benchmarks with many samples can be reduced to around 100 samples without sacrificing their utility. In addition, smaller benchmarks are advantageous because they are both cheaper to evaluate and impose a less significant environmental impact [76]. 3 CiteAgent We now describe CiteAgent, an LM-based system that we built to mimic researcher performance of open-ended citation attribution. A researcher seeking the correct attribution for a claim might use a search engine, read several papers, refine the search query, and repeat until successful. To allow CiteAgent to perform these actions, we built it to use Semantic Scholar to search for and read papers. Unless specified otherwise, we refer to CiteAgent with the GPT-4o backbone simply as CiteAgent throughout this paper. Given a text excerpt, we prompt CiteAgent to perform one of a fixed set of custom commands and provide the output that the given command generated. CiteAgent then gives its rationale before performing another action, following [90, 88]. Figure 3 shows this process. We now describe the starting prompt and custom agent commands. Prompt. Our prompt includes the task description, descriptions of available commands, and a demonstration trajectory, i.e., the series of actions that the system executes while solving an instance [90, 88]. The trajectory includes searching, reading a paper, and searching again (see Figure 4). We model our prompt on the SWE-Agent prompt [88]. Table 2: Commands available to the model using our system. | search(query, sort) read(ID) select(ID) | Searches for a query; sorts results by relevance or by citation count; returns a list of papers, where each item consists of the paper ID, title, number of citations, and abstract. Returns the full text of a paper, including title, author list, abstract, and the paper itself. Selects a paper from the search results as the answer. | | --- | --- | Agent Commands. CiteAgent can respond to three custom commands (see Table 2). It always begins by executing the search command (sorting by relevance or citation count), which searches Semantic Scholar for a query and returns top results in a sorted order. After searching, CiteAgent can either search again, read one of the listed papers, or select a paper can perform up to 15 actions for every sample. Once a select action is taken, the session ends, and the selected paper is recorded. Search. CiteAgent initiates a search command by querying Semantic Scholar [46]. We chose the Selenium API [63] over the Semantic Scholar API due to the former’s significantly better re-ranked queries and its ability to provide a uniform interface for both our model and human trajectory annotators. Selenium also lets us access features such as sorting search results by relevance and citation count, which our human trajectory annotators found particularly valuable. To ensure correctness, we filter out search results published after the excerpt’s source paper, and the source paper itself. We then give CiteAgent the top 10 search results, which include paper id, title, abstract, and citation count. <details> <summary>extracted/5974968/figures/trajectory.png Details</summary>  ### Visual Description \n ## Diagram: Research Process - Paper Identification ### Overview This diagram depicts a research process focused on identifying a cited paper. It shows a sequence of observations, search engine queries, and actions taken to locate the paper referenced in an initial text excerpt. The process is visually represented as a series of blocks with text and simulated search engine results. ### Components/Axes The diagram is structured into distinct blocks representing: * **Input:** The initial text excerpt. * **Observation:** Researcher's notes based on the input. * **Search Engine:** Simulated search engine interface displaying results. * **Thought + Action:** Actions taken by the researcher based on observations and search results. * **References:** List of references. There are no explicit axes in this diagram. The flow is primarily top-to-bottom. ### Detailed Analysis or Content Details **1. Input Block:** * Text: "In this section, we evaluate the localization ability of CAM when trained on the ILSVRC 2014 benchmark dataset [CITATION]" **2. First Observation Block:** * Text: "The cited paper seems to be the ImageNet dataset paper. We’ll do a search to make sure." **3. First Search Engine Block:** * Query: "ILSVRC 2014 benchmark" * Sort: "citations" * Results: 1. "Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition" - K He et al. 2. "A Scale Adaptive Kernel Correlation Filter Tracker with Feature Integration" - Y Li et al. **4. First Thought + Action Block:** * Text: "Paper “1” seems to directly mention the ILSVRC 2014 benchmark in the abstract" * Action: "read\_paper(“1”)" **5. Second Observation Block:** * Text: "Based on this work, we attended the competition of ILSVRC 2014 [26]" **6. References Block:** * "[26] O. Russakovsky et al., “Imagenet large scale visual recognition challenge”" **7. Second Search Engine Block:** * Query: "Imagenet large scale visual recognition challenge" * Results: 1. "Imagenet large scale visual recognition challenge" - O. Russakovsky et al. **8. Second Thought + Action Block:** * Text: "Based on the text and references read, the title is “Imagenet large scale visual recognition challenge”" * Action: "search(query=“Imagenet large scale visual recognition challenge”, sort=“citations”)" * Action: "select(“1”)" ### Key Observations * The process is iterative, involving observation, search, and refinement. * The researcher uses the initial citation context ("ILSVRC 2014 benchmark") to narrow down the search. * The search results are sorted by citations, suggesting a focus on influential papers. * The researcher uses both the title and authors to confirm the correct paper. * The diagram simulates a real-world research workflow. ### Interpretation This diagram illustrates a common research methodology: starting with a citation, identifying potential candidate papers through search engines, and iteratively refining the search based on observations and available information. The use of "Thought + Action" blocks highlights the cognitive process involved in research, where observations lead to specific actions. The sorting by "citations" indicates a preference for well-regarded and impactful publications. The diagram effectively demonstrates how a researcher can navigate the vast landscape of academic literature to locate a specific paper based on limited initial information. The diagram is not presenting data, but rather a process. It's a visual representation of a workflow. The diagram's value lies in its clarity and its ability to convey a complex process in a concise and understandable manner. </details> Figure 3: The demonstration trajectory we gave CiteAgent in the prompt. Read. Read command execution causes CiteAgent to retrieve the open-access PDF corresponding to the selected paper from Semantic Scholar. Using the PyPDF2 library [29], our system extracts the text from the PDF, excluding visual figures. It then presents the text to CiteAgent, which generates a thought and a new command. If an open-access PDF link is unavailable, CiteAgent returns a message to that effect. We note that due to the limited context length of 8K tokens in the LLaMA-3 LM, we excluded the read action when using that model. Select. Select command execution causes CiteAgent to choose a paper to attribute to the input text excerpt, which ends the run. If the number of actions reaches 14, CiteAgent is prompted to make a selection, forcefully concluding the run. This design choice ensures that all runs complete within a finite time and budget. 4 Experiment Setup Below, we provide detailed implementation information for the baseline models and the various CiteAgent configurations we used for our evaluations. SPECTER Models. We present the results of SPECTER [21] and SPECTER2 [77] on CiteME as our baselines. SPECTER [21] encodes robust document-level representations for scientific texts, achieving high performance on citation prediction tasks without the need for fine-tuning. We use the Semantic Scholar SPECTER API https://github.com/allenai/paper-embedding-public-apis to embed the input text excerpts and the Semantic Scholar Datasets API https://api.semanticscholar.org/api-docs/datasets to embed all papers on Semantic Scholar, using these embeddings as our retrieval set. SPECTER2 models [77] introduce task-specific representations, each tailored to different tasks. For our experiments, we use the base customization of SPECTER2 from Hugging Face https://huggingface.co/allenai/specter2 to embed text excerpts and the Semantic Scholar Datasets API to similarly embed all papers on Semantic Scholar, forming our retrieval set. We apply an exact kNN [53] match to identify the closest embedding, computing the cosine similarity between the embeddings of text excerpt and all available papers (title and abstract). Using exact kNN matches ensures no approximations/errors are introduced while matching queries. We embed the query text excerpt as title only and both title and abstract, but that did not change the performance of the SPECTER models. CiteAgent. We run the CiteAgent system with three SoTA LMs as backbones: GPT-4o [1], Claude 3 Opus [3], and LLaMa-3-70B [81]. We additionally ablate over three classes of commands (Table 2): 1. Search and Read. The model can perform both search and read commands. 1. Search Only. The model is not allowed to read papers but can perform searches. 1. No Commands. The model operates with no access to the interface for actions like searching and reading. Each class of actions is evaluated with and without demonstrations trajectories in the prompt, resulting in six configurations per LM. With three LMs, two action classes, and the option to include or exclude demonstrations, we present a total of 12 CiteAgent ablations. We exclude LLaMa with both Search and Read because its context length is limited to 8k tokens. For all experiments, we use a temperature of 0.95, following Yang et al. [88], and provide our detailed prompts in Appendix E. 5 Results Table 3: Performance of LMs (using our system) and retrieval methods on CiteME, summarized. | | GPT-4o | LLaMA-3-70B | Claude 3 Opus | SPECTER2 | SPECTER1 | | --- | --- | --- | --- | --- | --- | | Accuracy [%] | 35.3 | 21.0 | 27.7 | 0 | 0 | We present the evaluation results of the CiteME benchmark in Table 3. Our best model, CiteAgent (GPT-4o, search and read commands, and a demonstration in the prompt) achieves 35.3% accuracy, while the previous state-of-the-art models, SPECTER2 and SPECTER, achieve 0%. Human performance on the same task is 69.7% accuracy, with less than a minute of search time, indicating that a significant 34.4% gap remains. Table 4: Accuracy (in %) of LMs and retrieval methods on CiteME. We test how the available commands and prompt demonstrations affect CiteME performance. LLaMA’s context window is too small and therefore incompatible with the read command. | | Method | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | | GPT-4o | LLaMA-3-70B | Claude 3 Opus | SPECTER2 | SPECTER | | | | | Commands | No Commands | w/o Demo | 0 | 4.2 | 15.1 | 0 | 0 | | w/ Demo | 7.6 | 5.9 | 18.5 | – | – | | | | Search Only | w/o Demo | 26.1 | 21.0 | 26.1 | – | – | | | w/ Demo | 29.4 | 2.5 | 27.7 | – | – | | | | Search and Read | w/o Demo | 22.7 | N/A | 27.7 | – | – | | | w/ Demo | 35.3 | N/A | 26.1 | – | – | | | Performance across Language Models. Comparing the performance of LMs across columns in Table 4, GPT-4o demonstrates the highest accuracy when it has access to both read and search commands, outperforming other LMs by a wide margin. This finding aligns with previous research [88], which shows that GPT-4 powered agents excel in solving software issues. Notably, GPT-4o achieves high performance across settings even though CiteME consists exclusively of samples that GPT-4o cannot predict correctly without commands; its 0% performance without commands and demonstration trajectory is by design. However, LMs outperforming the SPECTER models purely by autoregressive generation provides evidence that LMs act as implicit knowledge bases with sufficient capacity [68]. Peformance across Demonstrations. Comparing the performance between w/o Demo and w/ Demo rows in Table 4, we observe that LLaMA and Claude surprisingly perform worse when provided with a demonstration trajectory in the prompt. This may be due to the increased prompt length, which complicates the detection of important information [52]. LLaMA-3-70b incurs a performance drop to 2.5% due to combined history extending beyond its context length, resulting in errors. However, GPT-4o effectively utilizes demonstrations, which improves its accuracy. Performance across Commands. GPT-4o is the only LM whose accuracy improves with access to more commands, allowing it to read full papers. CiteAgent with GPT-4o creatively uses its commands across test samples, demonstrating command behaviors not shown in the demonstration trajectory (see Figure 4). It frequently refines its searches based on previous results and occasionally reads multiple papers before making a selection. In contrast, Claude 3 Opus is less effective in utilizing additional commands, likely due to difficulties in detecting important information [52]. <details> <summary>extracted/5974968/figures/trajectory_analysis.png Details</summary>  ### Visual Description \n ## Diagram: Workflow Comparison ### Overview The image presents a visual comparison of three workflows, delineated by color-coded rectangular blocks. Each workflow consists of a sequence of actions represented by text labels within the blocks. The workflows are arranged in three vertical columns, visually separated by dashed lines. The diagram appears to illustrate different approaches to a task, potentially information retrieval or document processing. ### Components/Axes The diagram consists of three distinct workflows, visually separated by dashed vertical lines. Each workflow is represented by a series of rectangular blocks. The colors used are: * **Blue:** Workflow 1 * **Green:** Workflow 2 * **Red:** Workflow 3 Each block contains one of the following actions: * search sort=Citations * search sort=Relevance * read * select ### Detailed Analysis or Content Details **Workflow 1 (Blue):** The workflow consists of 6 blocks arranged vertically. 1. search sort=Citations 2. read 3. search sort=Citations 4. select 5. select 6. select **Workflow 2 (Green):** The workflow consists of 7 blocks arranged vertically. 1. search sort=Citations 2. search sort=Relevance 3. read 4. search sort=Relevance 5. search sort=Relevance 6. select 7. select **Workflow 3 (Red):** The workflow consists of 6 blocks arranged vertically. 1. search sort=Citations 2. search sort=Relevance 3. search sort=Relevance 4. read 5. read 6. select ### Key Observations * All three workflows begin with a "search" action, but differ in the sorting criteria ("Citations" or "Relevance"). * Workflow 2 is the longest, containing 7 steps, while Workflows 1 and 3 have 6 steps each. * The "read" action appears in all workflows, but its placement varies. * The "select" action appears multiple times in Workflows 1 and 2, but only once in Workflow 3. * Workflow 1 and 3 both start with "search sort=Citations" * Workflow 2 and 3 both have "search sort=Relevance" in the second step. ### Interpretation The diagram likely represents a comparison of different search and selection strategies. The varying sorting criteria ("Citations" vs. "Relevance") suggest different priorities in the search process. "Citations" might prioritize academic or authoritative sources, while "Relevance" might prioritize results that closely match the search query. The number of "select" actions could indicate the level of refinement or filtering applied in each workflow. The differences in workflow length and action sequence suggest that each approach has its own strengths and weaknesses. Workflow 2, being the longest, might be more thorough but also more time-consuming. Workflows 1 and 3 might be more efficient but potentially less comprehensive. The diagram doesn't provide quantitative data, so it's difficult to determine which workflow is "best." However, it highlights the trade-offs between different search and selection strategies and suggests that the optimal approach depends on the specific task and user needs. The diagram is a high-level illustration of process flows, and doesn't contain any numerical data or statistical analysis. </details> Figure 4: Five CiteAgent trajectories on five different samples. CiteAgent often exhibits behavior not shown in the demonstration given in the prompt, for example: searching by citation count and then by relevance, and searching multiple times in a row. Gray dotted box: prompt demonstration; green dotted boxes: CiteAgent succeeds; red dotted boxes: CiteAgent fails. 5.1 Error Analysis To better identify CiteAgent’s shortcomings, we analyze 50 randomly chosen CiteME samples from the best performing CiteAgent (using the GPT-4o backbone, with demonstrations, Search and Read commands) failed to solve correctly. We classify each error into three types based on CiteAgent’s searches, its predicted paper and the justification provided: Error Type 1: Misunderstands the Excerpt. This category accounts for 50% of the errors. It occurs when CiteAgent focuses on irrelevant parts of the excerpt or omits critical details. For example, in the following excerpt: {quoting} The pioneering work of Reed et al. [37] approached text-guided image generation by training a conditional GAN [CITATION], conditioned by text embeddings obtained from a pretrained encoder. CiteAgent searches for "Reed text-guided image generation conditional GAN" instead of "conditional GAN". It mistakes "Reed" as relevant to the current citation although it pertains to the previous one. Error Type 2: Understands the Excerpt but Stops Prematurely. In 32% of cases, CiteAgent searches for the correct term, but it stops at a roughly matching paper instead of the exact match. For example, in the following excerpt: {quoting} Using Gaussian noise and blur, [CITATION] demonstrate the superior robustness of human vision to convolutional networks, even after networks are fine-tuned on Gaussian noise or blur. CiteAgent found a paper comparing human and machine robustness but missed that it did not cover fine-tuned networks. Notably, this paper referenced the correct target paper, meaning CiteAgent could have found the right answer with just one more step if it had properly understood the paper it was reading. Moreover, in 12.5% of such cases, the correct paper appeared in the search results but was not chosen by CiteAgent. Error Type 3: Finds the Correct Citation but Stops Prematurely. The last 18% of errors occur when CiteAgent reads an abstract or paper and finds the correct citation; however, instead of doing another search, it selects the paper that cites the correct citation and stops searching. For example, in the following excerpt: {quoting} [CITATION] investigates transformers’ theoretical expressiveness, showing that transformers cannot robustly model noncounter-free regular languages even when allowing infinite precision. CiteAgent finds a paper discussing the target paper and reports it, but it stops at the citing paper instead of searching for the correct target paper. For instance, it reports: ".. specifically mentioning Hahn’s work on transformers’ classification decisions becoming ineffective over longer input strings. This fits well with the description in the excerpt.." but it selects the citing paper instead of finding Hahn’s work, which is the correct target paper. Technical Errors. Aside from comprehension errors that stem from a lack of understanding an excerpt, 5.8% of runs encountered technical issues. Occasionally, the LM formats responses incorrectly, making them unparseable by the system. Additionally, the Semantic Scholar API has inconsistencies, such as not providing open access PDF links when available or linking to non-existent web pages. Further details on these technical errors are provided in Appendix F. <details> <summary>extracted/5974968/figures/4o_claude.png Details</summary>  ### Visual Description \n ## Bar Charts: CiteAgent Command Frequency Comparison ### Overview The image presents two side-by-side bar charts comparing the command frequency of a "CiteAgent" system when used with two different Large Language Models (LLMs): GPT-4o and Claude 3 Opus. The charts display the frequency of three commands – "search(sort=Citations)", "search(sort=Relevance)", "read", and "select" – across different steps (1 to 15). The y-axis represents "Command Frequency", while the x-axis represents "Step". ### Components/Axes * **Titles:** * Left Chart: "CiteAgent with GPT-4o" * Right Chart: "CiteAgent with Claude 3 Opus" * **X-axis Label:** "Step" (ranging from 1 to 15) * **Y-axis Label:** "Command Frequency" (ranging from 0 to 40) * **Legend:** Located in the top-right corner of the right chart. * "search(sort=Citations)" - represented by a yellow color. * "search(sort=Relevance)" - represented by an orange color. * "read" - represented by a light grey color. * "select" - represented by a white color. ### Detailed Analysis or Content Details **Chart 1: CiteAgent with GPT-4o** * **search(sort=Citations) (Yellow):** The frequency starts at approximately 30 at Step 1, decreases to around 10 at Step 5, and continues to decline to approximately 2 at Step 15. * **search(sort=Relevance) (Orange):** The frequency starts at approximately 20 at Step 1, decreases to around 10 at Step 5, and continues to decline to approximately 2 at Step 15. * **read (Light Grey):** The frequency starts at approximately 10 at Step 1, decreases to around 5 at Step 5, and continues to decline to approximately 2 at Step 15. * **select (White):** The frequency starts at approximately 40 at Step 1, decreases to around 20 at Step 5, and continues to decline to approximately 4 at Step 15. **Chart 2: CiteAgent with Claude 3 Opus** * **search(sort=Citations) (Yellow):** The frequency starts at approximately 25 at Step 1, decreases to around 12 at Step 5, and continues to decline to approximately 2 at Step 15. * **search(sort=Relevance) (Orange):** The frequency starts at approximately 30 at Step 1, decreases to around 15 at Step 5, and continues to decline to approximately 2 at Step 15. * **read (Light Grey):** The frequency starts at approximately 10 at Step 1, decreases to around 5 at Step 5, and continues to decline to approximately 2 at Step 15. * **select (White):** The frequency starts at approximately 20 at Step 1, decreases to around 10 at Step 5, and continues to decline to approximately 2 at Step 15. ### Key Observations * Both charts exhibit a similar trend: all command frequencies decrease as the step number increases. This suggests that the CiteAgent system requires fewer commands as it progresses through the task. * The "select" command consistently has the highest frequency at the beginning of the process for both LLMs. * GPT-4o initially shows higher frequencies for all commands compared to Claude 3 Opus, particularly for "select" and "search(sort=Citations)". * The relative frequencies of the commands are similar between the two LLMs, with "select" being the most frequent, followed by "search(sort=Relevance)", "search(sort=Citations)", and then "read". ### Interpretation The data suggests that the CiteAgent system, regardless of the underlying LLM (GPT-4o or Claude 3 Opus), becomes more efficient as the process unfolds. The initial high frequency of the "select" command likely indicates the initial stages involve identifying relevant information. The subsequent decline in all command frequencies suggests that the system refines its search and selection criteria with each step, requiring less intervention. The higher initial frequencies observed with GPT-4o might indicate that it requires more initial prompting or exploration to achieve the same level of efficiency as Claude 3 Opus. However, both models converge towards similar command frequencies at later steps. This could imply that both models ultimately achieve comparable levels of task completion, but through slightly different approaches. The consistent ranking of command frequencies (select > search(relevance) > search(citations) > read) provides insight into the workflow of the CiteAgent system. It highlights the importance of initial selection, followed by relevance-based and citation-based searches, with "read" being the least frequent command, suggesting it's primarily used for final verification or detailed analysis. </details> Figure 5: CiteAgent trajectories on samples that were correctly predicted reveals differences in model behavior. GPT-4o reads more frequently than Claude 3 Opus and can correctly predict papers even after performing many actions. 5.2 Analyzing the Succesful Runs Manually examining the instances that were correctly predicted by GPT-4o and Claude 3 Opus (Figure 5) provides insights into how the LMs use commands they were given. First, we confirm the results presented in Table 4: GPT-4o frequently reads papers before it correctly predicts a citation. Second, when both LMs correctly predict a paper, they usually take just 5 steps or fewer to do so. This could stem from LMs loss of important details when given a long context window [52]. CiteAgent’s trajectories on CiteME enable us to analyze the shortcomings of GPT-4o and other SoTA LMs. These range from understanding fine details in text (Type 1 and Type 2 Errors), to not completely understanding the task (Type 3 Errors), to being unable to use commands (Technical Errors). Correcting these errors could improve the utility of LMs on CiteME and for other related tasks. 5.3 Benchmarking Reasoning Capability Improvements with Latest Models Table 5: Accuracy (in %) of newly released LMs on CiteME. | Commands | Method No Commands | w/o Demo | Claude-3.5-Sonnet 8.4 | LLaMa-3.1-70B 3.4 | o1-mini 16.0 | o1-preview 38.7 | | --- | --- | --- | --- | --- | --- | --- | | w/ Demo | 9.2 | 8.4 | 10.9 | – | | | | Search Only | w/o Demo | 36.1 | 29.4 | 25.2 | – | | | w/ Demo | 43.7 | 29.4 | 32.8 | – | | | | Search and Read | w/o Demo | 37.0 | 22.7 | 26.9 | – | | | w/ Demo | 40.3 | 27.7 | 34.5 | 61.3 | | | We compare the latest LLMs on the CiteME benchmark (Table 5) and find that Claude 3.5 Sonnet outperforms the previous best, Claude 3 Opus. This improvement stems from better generalization, as Sonnet achieves 9.2% without internet access, compared to Opus’ 18.5%. Similarly, LLaMa-3.1-70B shows significant gains of 8% over LLaMa-3.0-70B, highlighting enhanced reasoning capabilities. However, GPT-o1, while performing well on CiteME, appears to have memorized 38.7% of the dataset, making its 61.3% benchmark performance less clear in terms of true improvement compared to GPT-4o. 6 Related Work Recent work has made substantial progress in developing methods and datasets to assist researchers in paper writing and literature review [8, 12, 87] or act as tutors [18]. Early work [48, 56] showed that researchers automatically retrieved topics and papers considered highly relevant to their work. Other studies included methods that assist researchers in finding new ideas [34], understanding certain topics [62], provide expert answers backed up by evidence [55] or clarifying a paper’s related work by supplementing it with more information and focus [15, 67]. Closer to our line of research, prior studies developed methods for substantiating specific claims using evidence from published papers [75, 83, 85, 84, 91, 24, 39, 45]. Retrieval-augmented LMs [49, 11, 30] are also popularly used to ground claims with real-world evidence (see [60] for a survey). Chen et al. [16] built a web-based retrieval-augmented pipeline for fact verification; this contrasts with methods that use a static dataset for claim retrieval and verification [36, 5]. Concurrent to this work, Ajith et al. [2] build a retrieval benchmark consisting of questions about discoveries shown in specific machine learning papers. Paper discovery is a crucial component of systems that automate scientific research as shown in [10, 47, 54, 61, 78]. CiteME plays an important role in developing better tools for paper discovery, and provides a way to effectively measure their efficiency. Currently, these systems are tested as a whole, without isolating the tools responsible for scientific discovery. CiteME allows us to evaluate components within them independently – and we discover that current LM Agents are not yet ready for automated paper discovery, leading to serious gaps in end-to-end automated research pipelines. In addition, most existing LM benchmarks are saturated, with most LMs scoring 80-95% on them [43, 38, 20]. There is a need in the AI community to show what properties LMs currently lack, to show LM developers what aspects they should work on. On CiteME, the best LMs get less than 40%, clearly indicating to developers an important task that they could improve LMs on, while also providing an indicator they can use to track progress. Context-aware Recommendation. Relevant to our research focus, [57, 64, 37] take as input documents or parts thereof and recommend papers that are likely to be cited, often referred to as context-aware citation recommendation [51, 26, 89, 28, 42, 65, 33]. The text inputs we use in CiteME resemble those used in [42, 65, 80], which contain a few sentences with a masked out citation. However, CiteME differs because it uses excerpts containing only one unambiguous citation, making the context sufficient to identify the cited paper. Furthermore, our work explores agents with access to real-time paper information through tools like Semantic Scholar. This is crucial for real-time use since thousands of new papers are indexed by arXiv monthly (e.g., 8,895 papers in March 2024 under the cs category) [4]. Most previous approaches would be impractical due to the need for retraining with every new paper issuance. Citation Attribution Datasets. A variety of datasets contain text excerpts from scientific papers and corresponding citations [32, 31, 9, 40, 72, 44, 42, 33]. There are many crucial distinctions between the aforementioned datasets and CiteME, with the main one being that CiteME is composed of manually selected excerpts that clearly reference a paper. To our best knowledge, CiteME is the only dataset that reports human accuracy on the benchmark. Additionally, the excerpts in CiteME are mostly taken from papers published in the last few years (see Figure 2), whereas other datasets contain older papers. For example, the arXiv dataset [33] includes papers from 1991-2020, and FullTextPaperRead [42] contains papers from 2007-2017. This currency is particularly relevant in rapidly evolving fields like machine learning. The key distinction between the dataset and methods we present compared to previous works is their real life applicability. Our agent is based on SoTA LMs, needs no extra training, and can use a search engine, all of which make it easily applicable to real-world settings. 7 Conclusion This work introduces a citation attribution benchmark containing manually curated text excerpts that unambiguously refer to a single other paper. We posit that methods that succeed on CiteME are likely to be highly useful in assisting researchers with real-world ML-specific attribution tasks but also generally useful in finding sources for generic claims. Further, our CiteAgent autonomous system can search the Internet for and read papers, which we show to significantly enhance the abilities of LMs on CiteME. We anticipate that this work will lead to LMs that are more accurate research assistants in the vital scholarship tasks of attribution. Author Contributions The project was initiated by Andreas Hochlehnert and Ori Press, with feedback from Ameya Prabhu, Ofir Press, and Matthias Bethge. The dataset was created by Ori Press and Ameya Prabhu, with help from Vishaal Udandarao and Ofir Press. Experiments were carried out by Andreas Hochlehnert, with help from Ameya Prabhu. All authors contributed to the final manuscript. Acknowledgements The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting Ori Press, Andreas Hochlehnert, and Vishaal Udandarao. Andreas Hochlehnert is supported by the Carl Zeiss Foundation through the project “Certification and Foundations of Safe ML Systems”. Matthias Bethge acknowledges financial support via the Open Philanthropy Foundation funded by the Good Ventures Foundation. Vishaal Udandarao was supported by a Google PhD Fellowship in Machine Intelligence. Matthias Bethge is a member of the Machine Learning Cluster of Excellence, funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy – EXC number 2064/1 – Project number 390727645 and acknowledges support by the German Research Foundation (DFG): SFB 1233, Robust Vision: Inference Principles and Neural Mechanisms, TP 4, Project No: 276693517. This work was supported by the Tübingen AI Center. The authors declare no conflicts of interests. References - Achiam et al. [2023] Josh Achiam, Steven Adler, Sandhini Agarwal, Lama Ahmad, Ilge Akkaya, Florencia Leoni Aleman, Diogo Almeida, Janko Altenschmidt, Sam Altman, Shyamal Anadkat, et al. Gpt-4 technical report. arXiv preprint arXiv:2303.08774, 2023. - Ajith et al. [2024] Anirudh Ajith, Mengzhou Xia, Alexis Chevalier, Tanya Goyal, Danqi Chen, and Tianyu Gao. 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Effective large language model adaptation for improved grounding. arXiv preprint arXiv:2311.09533, 2023. - Zhang et al. [2023] Muru Zhang, Ofir Press, William Merrill, Alisa Liu, and Noah A Smith. How language model hallucinations can snowball. arXiv preprint arXiv:2305.13534, 2023. Appendix A Excerpts from Citation Datasets To demonstrate the problematic nature of automatically sourced text excerpts, we randomly choose 10 excerpts from FullTextPeerRead, ACL-200, RefSeer, and arXiv. We tag each sample chosen with one of 4 tags, as summarised in Table 1 in the main paper. We show each sample as it appears verbatim, using the datasets that appear in the official repository https://github.com/nianlonggu/Local-Citation-Recommendation of Gu et al. [33]. ACL-200 [9, 58] - m which the data was extracted (original). We used a combination of automatic (e.g. BLEU–4 (OTHERCIT), METEOR (OTHERCIT)) and human metrics (using crowdsourcing) to evaluate the output (see generally, TARGETCIT . However, in the interest of space, we will restrict the discussion to a human judgment task on output preferences. We found this evaluation task to be most informative for system improvement. The ta Unattributable - n Section 2 that it is more difficult to extract keyphrases correctly from longer documents. Second, recent unsupervised approaches have rivaled their supervised counterparts in performance (OTHERCIT; TARGETCIT b). For example, KP-Miner (OTHERCIT), an unsupervised system, ranked third in the SemEval-2010 shared task with an F-score of 25.2, which is comparable to the best supervised system scoring 27.5. 5 An Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. - rams include unigrams for all feature definitions and bigrams for selected ones. Figure 3b shows a sample of the actual extended set. We use two datasets, one prepared for the CoNLL 2000 shared task ( TARGETCIT and another prepared for the BioNLP/NLPBA 2004 shared task (OTHERCIT). They represent two different tagging tasks, chunking and named entity recognition, respectively. The CoNLL 2000 chunking dataset Trivial - ipts were from meetings, seminars and interviews. Some authors have also referred to this phenomenon as Ellipsis because of the elliptical form of the NSU [OTHERCIT, Fern´andez et al., 2004, OTHERCIT, TARGETCIT , OTHERCIT]. While the statistical approaches 336 have been investigated for the purpose of ellipsis detection [Fern´andez et al., 2004, OTHERCIT], it has been a common practice to use rules – syntact Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. - e source language is morphologically poor, such as English, and the target language is morphologically rich, such as Russian, i.e., language pairs with a high degree of surface realization ambiguity ( TARGETCIT . To address this problem we propose a general approach based on bilingual neural networks (BNN) exploiting source-side contextual information. This paper makes a number of contributions: Unlike previ Reasonable - n our approach and the one described in (OTHERCIT). Such a similarity is calculated by using the WordNet::Similarity tool (OTHERCIT), and, concretely, the Wu-Palmer measure, as defined in Equation 1 ( TARGETCIT . 2N3 Sim(C1, C2) ? (1) N1 + N2 + 2N3 where C1 and C2 are the synsets whose similarity we want to calculate, C3 is their least common superconcept, N1 is the number of nodes on the path from C1 to C3, Reasonable - ch detected image object a visual attribute and a spatial relationship to the other objects in the image. The spatial relationships are translated into selected prepositions in the resulting captions. TARGETCIT used manually segmented and labeled images and introduced visual dependency representations (VDRs) that describe spatial relationships between the image objects. The captions are generated using templ Reasonable - ous open source machine translation systems. The widely used Moses system (OTHERCIT) implements the standard phrase-based translation model. Parsingbased translation models are implemented by Joshua ( TARGETCIT , SAMT (OTHERCIT), and cdec (OTHERCIT). Cunei (OTHERCIT) implements statistical example-based translation. OTHERCIT and OTHERCIT respectively provide additional open-source implementations of phrase-b Trivial - and test set, we had about 1000 sentences each with 10 reference translations taken from the NIST 2002 MT evaluation. All Chinese data was re-segmented with the CRF-based Stanford Chinese segmenter ( TARGETCIT that is trained on the segmentation of the Chinese Treebank for consistency. The parser used in Section 3 was used to parse the training data so that null elements could be recovered from the trees. Trivial - rdering between nodes), their means of creation, and the scoring method used to extract the best consensus output from the lattice (OTHERCIT). In speech processing, phoneme or word lattices (OTHERCIT; TARGETCIT are used as an interface between speech recognition and understanding. Lat1318 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics, pages 1318–1327, Uppsala, Sweden Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. RefSeer [40, 58] - . Their experiments suggested that view independence does indeed affect the performance of co-training; but that CT, when compared to other algorithms that use labeled and unlabeled data, such as EM ( TARGETCIT ; OTHERCIT), may still prove e#ective even when an explicit feature split is unknown, provided that there is enough implicit redundancy in the data. In contrast to previous investigations of Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. - eeded is NP-hard. On the other hand, if the permutation $\pi$ avoids the pattern 1-2-3, no shuffles are needed if k $≥$ 5 (this is the result that every triangle free circle graph is 5-colorable, see again TARGETCIT ). It becomes clear once more why circle graphs “frustrated mathematicians for some years” OTHERCIT, and still continue to do so. 5 Stacking Constraints We finally consider the generalization in which ite Reasonable - a small number of details they have many things in common, especially the process of motion compensation and the DCT. Due to similar motion compensation the motion vector (MV) can be reused very well TARGETCIT . Furthermore, the equivalent usage of the DCT of block size ? ? makes a transcoder implementation within the DCT-domain possible OTHERCIT. With the standardization of H.264 the task of heterogeneous trans Reasonable - tioned Transactions ? Lingxiang Xiang Michael L. Scott Department of Computer Science, University of Rochester lxiang, scott@cs.rochester.edu 1. Introduction Twenty years after the initial proposal TARGETCIT , hardware transactional memory is becoming commonplace. All commercial versions to date—and all that are likely to emerge in the near future—are best effort implementations: a transaction may abort a Reasonable - local values generating a cluster are uniformly distributed in the range of [ $\mu_{ij}$ - $\sigma_{ij}$ $×$ 0.01, $\mu_{ij}$ + $\sigma_{ij}$ $×$ 0.01]. ? Irrelevant feature f ? j $∈$ $S_{i}$ : We uniformly generate values in the entire range TARGETCIT . We then synthetically generate co-occurrence scores. While the co-occurrence score can be arbitrarily generated, it is non-trivial to decide the ground-truth clusters when featurebased and co-occurr Unattributable - for visualizing the messagesow between objects in terms of method invocations. The scenario diagrams are generated from event traces and linked to other sources of information. Jerding and colleagues TARGETCIT , OTHERCIT focus on the interactions between program components at runtime. They observed that recurring interaction pattern can be used in the abstraction process for program understanding. The authors d Trivial: Though the cited excerpt cites more than one paper, that author name is given. - Many multimedia services, such as audio-video conferencing or video playback, have associated with them performance requirements that must be met to guarantee acceptable service to the users. TARGETCIT describes the requirements that some typical applications place on networks. The Tenet Real-Time Protocol Suite [Ferrari92 ] is one approach to providing these real-time performance guarantees in pac Unattributable - y of the controlled system is jeopardized. Several scheduling paradigms have been developed to support the analysis of a task set and determine if a schedule is feasible, e.g., rate-monotone analysis TARGETCIT . These scheduling paradigms rely on the assumption that the worst-case execution time (WCET) of hard real-time tasks be known a-priori. If the WCET of all tasks is known, it can be determined if a sc Reasonable - Recommended for acceptance by L. Quan. For information on obtaining reprints of this article, please send e-mail to: tpami@computer.org, and reference IEEECS Log Number TPAMI-0308-1003. æ recovered TARGETCIT , OTHERCIT. Note that these calibration techniques can be used for both central and noncentral catadioptric cameras. 2. Self-calibration. This kind of calibration techniques uses only point correspo Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. - ic controller in which a single action is associated with each node, and an observation results in a deterministic transition to a successor node (OTHERCIT; Hansen 1998; TARGETCIT a). In other cases, it is a stochastic controller in which actions are selected based on a probability distribution associated with each node, and an observation results in a probabilistic transition Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. arXiv [33] - In this study we parallelized the computation of gradients to improve the efficiency, and for large datasets further improvements can be obtained by using random minibatches to perform the inversion TARGETCIT . Such a strategy can be applied to any variational inference method (e.g. also ADVI) since variational methods solve an optimization rather than a stochastic sampling problem. In comparison, this st Unattributable - e been shown to provide superior generative quality, but VAEs have a number of advantages which include outlier robustness, improved training stability and interpretable, disentangled representations TARGETCIT . Disentangled representations are generally conceived to be representations in which each element relates to an independent (and usually semantically meaningful) generative factor OTHERCIT OTHERCIT . Achieving a di Reasonable - tion (NTF) OTHERCIT . For example, NMF/NTF-based ML methods have been successfully used for analysis of Monte Carlo simulated fission chamber’s output signals OTHERCIT , for compression of scientific simulation data TARGETCIT , and for a variety of other applications OTHERCIT . To avoid confusion, we should emphasize that in this paper the term tensor is used to define two different types of mathematical objects. We use tensors t Unattributable - insight about the generalization to the multipartite scenario, but also since the recovery problem for a tripartite probability distribution given all the three possible bipartite marginals is open OTHERCIT TARGETCIT OTHERCIT . Moreover, moving to the quantum scenario, also the compatibility problem for just a couple of overlapping marginals is open OTHERCIT OTHERCIT . We are then going to assume the set of the two given marginal densit Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. - seen that the proxy-SU(3) symmetry suggests N = 116 as the point of the prolate-to-oblate shape/phase transition, in agreement with existing exprerimental evidence OTHERCIT OTHERCIT OTHERCIT OTHERCIT OTHERCIT and microscopic calculations OTHERCIT OTHERCIT TARGETCIT OTHERCIT . Table 1 . Comparison between SU(3) irreps for U(6), U(10), U(15), and U(21), obtained by the code UNTOU3 OTHERCIT , contained in the relevant U(n) irrep for M valence protons or M valence neutrons. Above Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. - h cannot be explained by the traditional expected utility theory. In the context of decision-theoretic systems, Nadendla et al. have presented detection rules employed by prospect theoretic agents in TARGETCIT under different scenarios based on decision costs. In particular, the authors have focused on two types of prospect theoretic agents, namely optimists and pessimists, and have shown that the prospect Trivial: The name of the author of the referenced paper appears in the excerpt. - .) (3) $\psi$ ( $\land$ S ) does depend on the isotopy class of the collection. Its image in the space A( $\star$ k 1 ,… ,kµ ) , however, does not. These issues, and the above proof, are discussed in full detail in TARGETCIT . We remark that, in the form presented, this theorem does not depend on the two pieces of heavy machinery employed by OTHERCIT -it depends on neither the adapted Kirby-Fenn-Rourke theorem nor the OTHERCIT calculati Unattributable - ed to follows an addition rule 2ND 2 = analogous to that found for frequency conversion. A series of recent experiments demonstrated a more complex transfer of OAM in the generation of Raman sideband TARGETCIT OTHERCIT OTHERCIT . This process was found to follow a now wellestablished OAM-algebra for Stokes and anti-Stokes orders and was definitively verified through phase measurements in a simultaneous Young double slit e Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. - BMD. An important tool to assess the performance of decoding metrics is the generalized mutual information (GMI) OTHERCIT Sec. 2.4 ]. An interpretation of uniform BMD and bit-shaped BMD as a GMI are given in TARGETCIT and OTHERCIT , respectively. In OTHERCIT Sec. 4.2.4 ], the GMI is evaluated for a bit-metric. It is observed that the GMI increases when the bits are dependent. We call this approach shaped GMI. Besides the GMI, oth Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. - cay products dilute faster than matter, the expansion rate can be reduced around z $\sim$ 2.3. However, the simplest such model, a dark matter component decaying into dark radiation with constant lifetime TARGETCIT OTHERCIT , is in conflict with observations of the late integrated SachsWolfe effect and lensing power spectrum OTHERCIT OTHERCIT . Moreover, we find $\Omega$ ExDE becomes positive again at z < 1.5. Thus any decaying component mus Ambiguous: The citation is ambiguous by definition, as the excerpt cites more than one paper. FullTextPeerRead [42] - tion function: r=g.The typical training criterion for autoencoders is minimizing the reconstruction error, $\Sigma$ x $∈$ XL with respect to some loss L, typically either squared error or the binary cross-entropy TARGETCIT .Denoising autoencoders are an extension of autoencoders trained to reconstruct a clean version of an input from its corrupted version . The denoising task requires the network to learn representatio Ambiguous: Although [7] is cited, it could be argued that the original paper that used cross entropy as a loss [23] should be used. - al matrices of parameters, and show that it outperforms the random counterpart when applied to the problem of replacing one of the fully connected layers of a convolutional neural network for ImageNet TARGETCIT . Interestingly, while the random variant is competitive in simple applications , the adaptive variant has a considerable advantage in more demanding applications .The adaptive SELLs, including Adapti Trivial - eneous information networks. Recently, u peek_meaning:NTF . peek_catcode:NTF a . . published a question answering algorithm that converts a given question into a vector space model to find the answer TARGETCIT , but, like neural network based models 2013 , the learned model is generally uninterpretable. peek_meaning:NTF . peek_catcode:NTF a . . proposed T-verifier, a search engine based fact checker 2011 Ambiguous: The cited paper is [35], while [41] also fits the description given. - he graph’s main component correctly. The state-of-the-art described in gives a lowest value at 58, with the best algorithms around 60, while algorithms regularized spectral methods such as the one in TARGETCIT obtain about 80 errors.The current result should also extend directly to a slowly growing number of communities . It would be interesting to extend the current approach to smaller sized communities or Unattributable - amming approach that was used in all other structural tractability results that were known before, and as we have seen this is no coincidence. Instead, $\mathrm{B}$ -acyclic #SAT lies outside the STV-framework of TARGETCIT that explains all old results in a uniform way.We close this paper with several open problems that we feel should be explored in the future. First, our algorithm for #SAT is specifically designed for Unattributable - our method on a fully-connected network , we compare our method with on this dataset. CIFAR and SVHN dataset, we evaluate our method on three popular network architectures: VGGNet, Net and DenseNet TARGETCIT . The VGGNet is originally designed for ImageNet classification. For our experiment a variation of the original VGGNet for CIFAR dataset is taken from . For Net, a 164-layer pre-activation Net with bo Trivial - ars, various probabilistic extensions of description logics have been investigated, see, for instance,.The one that is closest to our approach is the type 1 extension of ALC proposed in the appendixof TARGETCIT . Briefly, This difference is the main reason why the ExpTime algorithm proposed by tz and Schrödercannot be transferred to our setting. It does not suffice to consider the satisfiable types independ Unattributable - h we compute through current input and the previous hidden state. The final output of hidden state would be calculated based on memory cell and forget gate.In our experiment we used model discussed in TARGETCIT .t x is feature vector for tth word in a sentence and hl is previous hidden state then computation of hidden and output layer of LSTM would be.Where $\sigma$ is sigmoid activation function, $\star$ is a element Unattributable - e use of conditional LSTMs in the generation component of neural network -based dialogue systems which depend on multiple conditioning sources and optimising multiple metrics.ral conversational agents TARGETCIT are direct extensions of the sequence-to-sequence model in which a conversation is cast as a source to target transduction problem.wever, these models are still far from real world applications becau Ambigiuous: The cited paper is [82], though [79] also fits the description given. - onsistent with previous findings.As a comparison we also include test performances of a BNN with a Gaussian approximation , a BNN with HMC, and a sparse Gaussian process model with 50 inducing points TARGETCIT . In test-LL metric our best dropout model out-performs the Gaussian approximation method on almost all datasets, and for some datasets is on par with HMC which is the current gold standard for yesian Ambiguous: The cited paper is [13], while [14] also fits the description given. A.1 Automatic Ambiguity Analysis In addition to the manual analysis above, we conducted an automated analysis of the ambiguous category. Specifically, we identified excerpts that cited multiple papers simultaneously (e.g., \cite{paper1, paper2, paper3}) where one of the cited papers is the target. This analysis allowed us to establish a lower bound on ambiguous excerpts across all benchmarks (Table 6). These excerpts can not serve well as questions since they have multiple different correct answers, whereas the respective benchmarks only include one correct target answer (as in CiteME). Table 6: Dataset ambiguity percentages from an automatic analysis. We note that this is just a lower bound estimate, as the automatic parsing is only able to detect a subset of the ambiguous excerpts. Still, these findings are consistent with our previous results, and show that previous benchmarks contain vast quantities of ambiguous excerpts. | arXiv | 54.96 | | --- | --- | | ACL | 27.20 | | RefSeer | 12.61 | FulllTextPeerRead automatically deletes all other citations, so this was not possible to do in their case. We have updated Table 1 in the revised draft with the results with the expanded 50-sample sets and included the automatic evaluation data. Appendix B Additional Comparison to Existing Benchmarks We additionally compare CiteME to previous benchmarks based on information found in [33]. Importatnly, CiteME differs from previous work in that the query set, from which the answers come from, is by far the largest with 218 million papers. Additionally, CiteME makes the entire paper available to the model, and not just a snippet. These two factors make CiteME able to mimic the experience a research would have when looking for papers. Table 7: Comparison of previous benchmarks and CiteME based on query set size, availability of full paper text, and date range. | FullTextPeerRead [42] | 5K | ✗ | ’07-’17 | | --- | --- | --- | --- | | ACL-200 [9, 58] | 20K | ✗ | ’09-’15 | | RefSeer [40, 58] | 625K | ✗ | Unk - ’14 | | arXiv [33] | 1.7M | ✗ | ’91-’20 | | CiteME (Ours) | 218M | ✓ | ’08-’24 | Appendix C CiteAgent Results By Year Language models may perform better on papers they encountered during training, with a drop in performance on newer papers, leading to better performance from more recently released models. To test this, we compare the results of our CiteAgent on excerpts from papers published before 2024 versus on excerpts from papers published in 2024. We note that the cutoff dates for Claude 3 Opus, Claude 3.5 Sonnet and GPT-4o are August 2023, August 2023 and October 2023 respectively. The results, shown in Table 8, show that this is indeed true for the LMs analyzed in this paper. Table 8: Accuracy of CiteAgent models (in %) on questions where the target papers were published either before 2024 and during 2024 | CiteAgent + GPT-4 | 36.99% | 32.61% | | --- | --- | --- | | CiteAgent + Claude 3 Opus | 28.77% | 21.74% | | CiteAgent + Claude 3.5 Sonnet | 42.47% | 36.96% | Appendix D Verifying GPT-4 Paper Tags We asked GPT-4 to generate 3 general tags that describe every paper in CiteME. We manually verify that the tags automatically generated by GPT-4 are overwhelmingly correct. Here, we give a few examples of papers, and their matching tags: - Paper Name: PaLI: A Jointly-Scaled Multilingual Language-Image Model Tags: Multimodal AI Models, Vision-Language Integration, Scalable Machine Learning - Paper Name: Grokking: Generalization Beyond Overfitting on Small Algorithmic Datasets Tags: Neural Network Generalization, Deep Learning Optimization, Algorithmic Data Analysis - Paper Name: Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack Tags: Adversarial Machine Learning, Neural Network Security, Robustness Evaluation] - Paper Name: Mamba-R: Vision Mamba ALSO Needs Registers Tags: Computer Vision Models, Image Processing Techniques, Neural Network Architectures - Mass-Editing Memory in a Transformer Tags: Language Model Updating, Natural Language Processing, Machine Learning Techniques Appendix E Example Trajectory Please note that the abstracts retrieved in the paper search have been redacted, and only the first two of the ten results are displayed to condense the text. Additionally, the content retrieved by the paper reading action has been abbreviated. The system prompt is omitted for brevity. Complete and exact prompts are available in the published code. User: You are now given an excerpt. Find me the paper cited in the excerpt, using the tools described above. Please make sure that the paper you select really corresponds to the excerpt: there will be details mentioned in the excerpt that should appear in the paper. If you read an abstract and it seems like it could be the paper we’re looking for, read the paper to make sure. Also: sometimes you’ll read a paper that cites the paper we’re looking for. In such cases, please go to the references in order to find the full name of the paper we’re looking for, and search for it, and then select it. Our implementation for ImageNet follows the practice in [21, 41]. The image is resized with its shorter side randomly sampled in [256, 480] for scale augmentation [41]. A 224×224 crop is randomly sampled from an image or its horizontal flip, with the per-pixel mean subtracted [21]. The standard color augmentation in [21] is used. We adopt batch normalization (BN) [CITATION] right after each convolution and before activation. Assistant: { "reason": "The excerpt mentions batch normalization (BN) and places it right after each convolution and before activation. I’ll search for ’batch normalization’ to find the appropriate citation.", "action": { "name": "search_relevance", "query": "batch normalization" } } User: - Paper ID: 995c5f5e62614fcb4d2796ad2faab969da51713e Title: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift Abstract: <redacted> Citation Count: 39617 - Paper ID: 713bbd21bd4780fc92a69645a8bee2a005cc90dd Title: Comparison of normalization algorithms for cross-batch color segmentation of histopathological images Abstract: <redacted> Citation Count: 18 <Remaining 8 Results> Assistant: { "reason": "The paper with ID 995c5f5e62614fcb4d2796ad2faab969da51713e titled ’Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift’ is explicitly about batch normalization and aligns perfectly with the mention of batch normalization in the given excerpt.", "action": { "name": "read", "paper_id": "995c5f5e62614fcb4d2796ad2faab969da51713e" } } User: arXiv:1502.03167v3 [cs.LG] 2 Mar 2015BatchNormalization: AcceleratingDeepNetworkTrainingb y ReducingInternalCovariateShift Sergey Ioffe GoogleInc., sioffe@google.comChristianSzegedy GoogleInc., szegedy@google.com Abstract TrainingDeepNeuralNetworksiscomplicatedbythefact that the distributionofeach layer’sinputschangesduring training, as the parametersof the previouslayers change. This slows down the training by requiringlower learning <Remaining Content of the Paper> Assistant: { "reason": "The paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift by Sergey Ioffe and Christian Szegedy precisely matches the description in the excerpt given. It details the application of batch normalization, placing it right after each convolution and before activation.", "action": { "name": "select", "paper_id": "995c5f5e62614fcb4d2796ad2faab969da51713e" } } Appendix F Technical Errors In this section, we break down the types of technical errors that can occur when running CiteAgent with a GPT-4o or Claude 3 Opus backbone. <details> <summary>extracted/5974968/figures/technical_errors/error_final_prompt.png Details</summary>  ### Visual Description \n ## Pie Charts: Error Analysis of GPT-4o and Claude Opus ### Overview The image presents two pie charts side-by-side, comparing the error types of GPT-4o and Claude Opus models during a "Search and Read w/ Demo" evaluation. Each chart visualizes the distribution of different error categories as percentages and absolute counts. ### Components/Axes Each pie chart has the following components: * **Title:** Indicates the model being analyzed ("Errors GPT-4o (Search and Read w/ Demo)" and "Errors Claude Opus (Search and Read w/ Demo)"). * **Slices:** Represent different error types. * **Labels:** Each slice is labeled with the error type and its percentage of the total errors, along with the absolute count in parentheses. * **Color Coding:** Each error type is assigned a distinct color for visual differentiation. ### Detailed Analysis or Content Details **GPT-4o Errors (Left Chart):** * **Correct:** 35.3% (42) - Represented by a light green slice. * **Wrong:** 58.8% (70) - Represented by a red slice. * **Max Actions Error:** 2.5% (3) - Represented by a light blue slice. * **Max Context Length Error:** 2.5% (3) - Represented by a yellow slice. * **Invalid JSON:** 0.8% (1) - Represented by a pink slice. **Claude Opus Errors (Right Chart):** * **Correct:** 26.1% (31) - Represented by a light green slice. * **Wrong:** 27.7% (33) - Represented by a red slice. * **Invalid JSON:** 42.9% (51) - Represented by a dark grey slice. * **Content Policy Error:** 3.4% (4) - Represented by a teal slice. ### Key Observations * GPT-4o has a higher percentage of "Wrong" answers (58.8%) compared to Claude Opus (27.7%). * Claude Opus has a significantly higher percentage of "Invalid JSON" errors (42.9%) than GPT-4o (0.8%). * GPT-4o has a more even distribution of errors across different categories (Max Actions, Max Context Length, Invalid JSON) compared to Claude Opus. * Both models have a substantial portion of errors categorized as "Wrong". * The "Correct" responses are higher for GPT-4o (35.3%) than for Claude Opus (26.1%). ### Interpretation The data suggests that GPT-4o, while making more incorrect responses overall, exhibits a more diverse range of error types. Claude Opus, on the other hand, struggles significantly with generating valid JSON, which constitutes the majority of its errors. This could indicate differences in the models' architectures, training data, or specific strengths and weaknesses in handling structured data formats. The higher percentage of "Wrong" answers for both models suggests a need for improvement in their reasoning and factual accuracy during search and read tasks. The "Content Policy Error" for Claude Opus, while small, indicates potential issues with adhering to safety guidelines. The difference in the "Correct" response rate suggests GPT-4o performs better overall in this specific "Search and Read w/ Demo" evaluation. The absolute counts provide context to the percentages, showing that the evaluation involved a reasonable number of samples (totaling 112 for GPT-4o and 119 for Claude Opus). </details> Figure 6: Different technical errors for the CiteAgent with Search and Read command with Demo comparing the GPT-4o and Claude Opus backbone. Claude Opus has a significantly higher error rate. It struggles to adhere to the expected JSON fromat and in four cases the content filter was triggered. <details> <summary>extracted/5974968/figures/technical_errors/error_zero_shot_search.png Details</summary>  ### Visual Description \n ## Pie Charts: Errors GPT-4o and Claude Opus (Search and Read w/o Demo) ### Overview The image presents two pie charts side-by-side, comparing the error rates of GPT-4o and Claude Opus models during a "Search and Read w/o Demo" task. Each chart visualizes the distribution of "Correct", "Wrong", and (for Claude Opus) "Invalid JSON" responses. ### Components/Axes Each chart lacks explicit axes, as it's a pie chart representing proportions. The charts are labeled with the model name and task description at the top. Each slice of the pie chart is labeled with the category name and percentage, along with the absolute count in parentheses. * **GPT-4o Chart:** * Categories: "Correct", "Wrong" * Colors: Light Green, Red * **Claude Opus Chart:** * Categories: "Correct", "Wrong", "Invalid JSON" * Colors: Light Green, Red, Dark Blue ### Detailed Analysis or Content Details **GPT-4o Chart:** * **Correct:** 22.7% (27) - Represented by a light green slice. * **Wrong:** 77.3% (92) - Represented by a red slice. * The red slice dominates the chart, indicating a significantly higher proportion of incorrect responses. **Claude Opus Chart:** * **Correct:** 27.7% (33) - Represented by a light green slice. * **Wrong:** 65.5% (78) - Represented by a red slice. * **Invalid JSON:** 6.7% (8) - Represented by a dark blue slice. * The red slice is the largest, but smaller than the GPT-4o's "Wrong" slice. * The "Invalid JSON" slice is relatively small, but represents a distinct error type. ### Key Observations * Both models exhibit a higher error rate ("Wrong" responses) than correct responses. * GPT-4o has a higher percentage of "Wrong" responses (77.3%) compared to Claude Opus (65.5%). * Claude Opus has an additional error category, "Invalid JSON", which accounts for 6.7% of its responses. * The absolute counts (in parentheses) provide context to the percentages, showing the sample size for each model. ### Interpretation The data suggests that both GPT-4o and Claude Opus struggle with the "Search and Read w/o Demo" task, producing more incorrect responses than correct ones. GPT-4o appears to be less accurate than Claude Opus in this specific scenario, as evidenced by its higher "Wrong" response rate. The presence of "Invalid JSON" errors in Claude Opus indicates a potential issue with its output formatting, possibly related to the search and read process. The task description "Search and Read w/o Demo" implies that the models are being evaluated on their ability to extract information from a source and provide a response, without the benefit of a demonstration or example. This could be a challenging task, requiring strong natural language understanding and reasoning capabilities. The high error rates suggest that both models still have room for improvement in these areas. The difference in error types (JSON validity for Claude Opus) could indicate differences in the models' architectures or training data. Further investigation would be needed to understand the root causes of these errors and develop strategies to mitigate them. The relatively small sample sizes (27 for GPT-4o correct, 33 for Claude Opus correct) should be considered when interpreting these results; larger sample sizes would provide more statistically significant conclusions. </details> Figure 7: Different technical errors for the CiteAgent with Search and Read command without Demo comparing the GPT-4o and Claude Opus backbone. Because there is no demo the system prompt is much shorter just containing the task description and the format instructions. One can see that the JSON error rate for Claude Opus is now drastically reduced. GPT-4o also exhibits a smaller error rate but its performance is degraded. <details> <summary>extracted/5974968/figures/technical_errors/error_search_only_demo.png Details</summary>  ### Visual Description ## Pie Charts: Error Analysis of Large Language Models ### Overview The image presents three pie charts, each representing the error distribution for a different Large Language Model (LLM): GPT-4o, Claude Opus, and Llama-3 70B. All models were evaluated using a "Search Only w/ Demo" methodology. Each pie chart categorizes errors into "Correct", "Wrong", "Invalid JSON", and "Max Actions Error" (or "Max Context Length Error" for Llama-3). The charts also display the percentage and count of each error type. ### Components/Axes Each chart has the following components: * **Title:** Indicates the LLM being analyzed and the evaluation methodology. * **Pie Slices:** Represent the proportion of each error category. * **Labels:** Each slice is labeled with the error category and its percentage and count (in parentheses). * **Color Coding:** Each error category is assigned a distinct color. ### Detailed Analysis or Content Details **GPT-4o (Search Only w/ Demo)** * **Correct:** 29.4% (35) - Represented by a green slice. * **Wrong:** 69.7% (83) - Represented by a red slice. * **Invalid JSON:** 0.9% (1) - Represented by a blue slice. **Claude Opus (Search Only w/ Demo)** * **Correct:** 27.7% (33) - Represented by a green slice. * **Wrong:** 62.2% (74) - Represented by a red slice. * **Invalid JSON:** 8.4% (10) - Represented by a blue slice. * **Max Actions Error:** 1.7% (2) - Represented by an orange slice. **Llama-3 70B (Search Only w/ Demo)** * **Wrong:** 6.7% (8) - Represented by a red slice. * **Invalid JSON:** 2.5% (3) - Represented by a blue slice. * **Max Context Length Error:** 89.9% (107) - Represented by an orange slice. * **Correct:** Not explicitly shown, but implied to be the remaining percentage. ### Key Observations * GPT-4o and Claude Opus have a significant proportion of "Wrong" answers, around 70% and 62% respectively. * Llama-3 70B exhibits a drastically different error profile, with the overwhelming majority of errors being "Max Context Length Error" (almost 90%). * Invalid JSON errors are relatively low for all models, except for Claude Opus, which has 8.4%. * GPT-4o has the lowest percentage of correct answers (29.4%) among the three models. * Claude Opus has the highest percentage of correct answers (27.7%) among the three models. ### Interpretation The data suggests that GPT-4o and Claude Opus struggle with providing accurate responses ("Wrong" errors) when using the "Search Only w/ Demo" methodology. The high percentage of "Wrong" answers indicates a potential issue with the models' ability to effectively utilize search results or generate correct outputs based on the provided context. The relatively low "Invalid JSON" error rate suggests that the models are generally capable of producing valid JSON output when required. Llama-3 70B, however, presents a different challenge. The dominant "Max Context Length Error" suggests that the model is frequently exceeding its context window during the search and demo process. This could be due to the complexity of the search queries, the length of the demo content, or limitations in the model's context handling capabilities. The low "Wrong" error rate suggests that when Llama-3 *can* process the information within its context window, it tends to generate more accurate responses. The differences in error profiles highlight the unique strengths and weaknesses of each model. GPT-4o and Claude Opus appear to be more prone to factual inaccuracies, while Llama-3 70B is limited by its context window. The "Search Only w/ Demo" methodology may be particularly challenging for Llama-3 70B, potentially requiring strategies to reduce the amount of information processed within a single context window. </details> Figure 8: Different technical errors for the CiteAgent with Search Only command with Demo comparing the GPT-4o, Claude Opus and LLaMA-3 70B backbone. The system prompt containing the Demo takes up a considerable amount of LLaMA-3’s context length therefore just a few actions lead to the model running out of context. <details> <summary>extracted/5974968/figures/technical_errors/error_search_only_no_demo.png Details</summary>  ### Visual Description \n ## Pie Charts: Error Analysis of Large Language Models ### Overview The image presents three pie charts, each representing the error distribution for a different Large Language Model (LLM): GPT-4o, Claude Opus, and LLaMA-3 70B. All models were tested using "Search Only w/o Demo" conditions. The charts categorize errors into three types: "Correct", "Wrong", and "Invalid JSON". The LLaMA-3 70B chart also includes a note about "Max Context Length Error". ### Components/Axes Each chart has the following components: * **Title:** Indicates the LLM being analyzed and the testing conditions. * **Pie Slices:** Represent the proportion of each error type. * **Labels:** Each slice is labeled with the error type ("Correct", "Wrong", "Invalid JSON") and the count/percentage. * **Colors:** * Correct: Light Green * Wrong: Red * Invalid JSON: Dark Brown/Orange ### Detailed Analysis or Content Details **1. GPT-4o (Search Only w/o Demo)** * **Correct:** 26.1% (31) - Light Green slice. * **Wrong:** 73.9% (88) - Red slice. * **Trend:** The vast majority of errors are "Wrong" responses. **2. Claude Opus (Search Only w/o Demo)** * **Correct:** 26.1% (31) - Light Green slice. * **Wrong:** 67.2% (80) - Red slice. * **Invalid JSON:** 6.7% (6) - Dark Brown/Orange slice. * **Trend:** Similar to GPT-4o, "Wrong" responses dominate, but Claude Opus also has a small percentage of "Invalid JSON" errors. **3. LLaMA-3 70B (Search Only w/o Demo)** * **Correct:** 21.0% (25) - Light Green slice. * **Wrong:** 52.9% (65) - Red slice. * **Invalid JSON:** 26.1% (31) - Dark Brown/Orange slice. * **Note:** "Max Context Length Error" is mentioned in the title. * **Trend:** LLaMA-3 70B has the lowest percentage of "Correct" responses and a significant proportion of "Invalid JSON" errors. ### Key Observations * All three models exhibit a high error rate, with "Wrong" responses being the most frequent type. * Claude Opus has the lowest proportion of "Wrong" responses compared to GPT-4o and LLaMA-3 70B. * LLaMA-3 70B has the highest proportion of "Invalid JSON" errors, and the lowest proportion of "Correct" responses. * The "Max Context Length Error" note suggests that LLaMA-3 70B may be particularly susceptible to issues related to input length. ### Interpretation The data suggests that all three LLMs struggle with accuracy when performing search-only tasks without a demonstration. The high rate of "Wrong" responses indicates that the models often provide incorrect or irrelevant information. The presence of "Invalid JSON" errors, particularly in LLaMA-3 70B, suggests issues with the model's ability to generate valid structured data. The differences between the models suggest varying strengths and weaknesses. Claude Opus appears to be more reliable in providing correct responses, while LLaMA-3 70B may be more prone to generating invalid JSON and is potentially limited by context length. The "Search Only w/o Demo" condition is crucial. The lack of a demonstration may hinder the models' ability to understand the desired output format or reasoning process. The results highlight the importance of providing clear instructions and examples to improve the performance of LLMs. The "Max Context Length Error" for LLaMA-3 70B suggests that the model's performance degrades when dealing with longer input sequences, which is a common limitation of transformer-based models. </details> Figure 9: Different technical errors for the CiteAgent with Search Only command without Demo comparing the GPT-4o, Claude Opus and LLaMA-3 70B backbone. <details> <summary>extracted/5974968/figures/technical_errors/error_final_prompt2.png Details</summary>  ### Visual Description \n ## Pie Charts: Error Analysis of LLM Responses ### Overview The image presents four pie charts, each representing the error distribution for a different Large Language Model (LLM) when performing a "Search and Read w/ Demo" task. The errors are categorized into "Correct", "Wrong", "Invalid JSON", and "Max Context Length Error" or "Max Actions Error". Each chart displays the percentage and count of each error type. ### Components/Axes Each chart has the following components: * **Title:** Indicates the LLM being analyzed (ol Mini, ol Mini, Claude 3.5 Sonnet, LLAMA-3.17B) and the task context ("Errors" + model name + " (Search and Read w/ Demo)"). * **Pie Slices:** Represent the proportion of each error category. * **Labels:** Each slice is labeled with the error category and its percentage and count (e.g., "Correct (61.7%)", "Wrong (49)"). * **Color Coding:** Each error category is assigned a specific color: * Correct: Green * Wrong: Red * Invalid JSON: Yellow * Max Context Length Error/Max Actions Error: Orange ### Detailed Analysis or Content Details **Chart 1: Errors ol Mini (Search and Read w/ Demo)** * Correct: 61.7% (71) * Wrong: 36.7% (42) * Invalid JSON: 1.2% (1) * Max Context Length Error: 0.4% (0) **Chart 2: Errors ol Mini (Search and Read w/ Demo)** * Correct: 34.5% (41) * Wrong: 63.6% (73) * Invalid JSON: 1.9% (2) **Chart 3: Errors Claude 3.5 Sonnet (Search and Read w/ Demo)** * Correct: 37.8% (45) * Wrong: 40.3% (48) * Invalid JSON: 21.9% (26) **Chart 4: Errors LLAMA-3.17B (Search and Read w/ Demo)** * Correct: 27.7% (33) * Wrong: 42.9% (51) * Invalid JSON: 12.6% (15) * Max Actions Error: 12.6% (15) * Max Context Length Error: 4.2% (5) ### Key Observations * **ol Mini** shows the highest percentage of correct responses in the first chart (61.7%), but a significantly lower percentage in the second chart (34.5%). * **Claude 3.5 Sonnet** has a substantial proportion of "Invalid JSON" errors (21.9%). * **LLAMA-3.17B** exhibits a more distributed error profile, with significant percentages for "Wrong", "Invalid JSON", and "Max Actions Error". * The "Max Context Length Error" is only present in the first and last charts, and is a small percentage of the total errors. ### Interpretation The data suggests varying performance levels across the different LLMs on the "Search and Read w/ Demo" task. The significant difference in performance for "ol Mini" between the two charts could indicate variations in the input data or experimental setup. The high rate of "Invalid JSON" errors for "Claude 3.5 Sonnet" suggests a potential issue with its JSON formatting capabilities. "LLAMA-3.17B" appears to struggle with a broader range of errors, including generating incorrect responses, formatting errors, and exceeding context limits. The presence of "Max Context Length Error" and "Max Actions Error" indicates that the models sometimes encounter limitations in handling the complexity or length of the input or the required actions. The charts provide a comparative overview of the error profiles, highlighting the strengths and weaknesses of each LLM in this specific task. Further investigation would be needed to understand the root causes of these errors and to improve the performance of the models. </details> Figure 10: Different technical errors for the CiteAgent with Search and Read command with Demo comparing the o1-Preview, o1-Mini, Claude 3.5 Sonnet and LLaMA-3.1 70B backbone. <details> <summary>extracted/5974968/figures/technical_errors/error_search_and_read_no_demo.png Details</summary>  ### Visual Description ## Pie Charts: Error Analysis of LLM Performance ### Overview The image presents three pie charts comparing the error types of three different Large Language Models (LLMs): `ol 1 Mini`, `Claude 3.5 Sonnet`, and `LLAMA-3-170B`. All models were evaluated on "Search and Read w/o Demo" tasks. Each pie chart visualizes the distribution of errors into four categories: "Correct", "Wrong", "Max Context Length Error", and "Invalid JSON". The charts also display the percentage and count of each error type. ### Components/Axes Each chart has the following components: * **Title:** Indicates the LLM being analyzed (e.g., "Errors ol 1 Mini (Search and Read w/o Demo)") * **Pie Slices:** Represent the proportion of each error type. * **Labels:** Each slice is labeled with the error type and its percentage and count (e.g., "Correct 26.9% (32)"). * **Color Coding:** Each error type is assigned a specific color: * Correct: Green * Wrong: Red * Max Context Length Error: Blue * Invalid JSON: Pink ### Detailed Analysis or Content Details **Chart 1: Errors ol 1 Mini (Search and Read w/o Demo)** * **Correct:** 26.9% (32) - Green slice, occupying approximately one-quarter of the pie. * **Wrong:** 68.1% (81) - Red slice, dominating the pie chart. * **Max Context Length Error:** 1.7% (2) - Blue slice, a very small portion. * **Invalid JSON:** 3.4% (4) - Pink slice, a small portion. **Chart 2: Errors Claude 3.5 Sonnet (Search and Read w/o Demo)** * **Correct:** 37.0% (44) - Green slice, slightly more than one-third of the pie. * **Wrong:** 52.9% (63) - Red slice, the largest portion of the pie. * **Max Context Length Error:** 0.8% (1) - Blue slice, a very small portion. * **Invalid JSON:** 9.2% (11) - Pink slice, a noticeable portion. **Chart 3: Errors LLAMA-3-170B (Search and Read w/o Demo)** * **Correct:** 22.7% (27) - Green slice, less than one-quarter of the pie. * **Wrong:** 58.0% (69) - Red slice, the largest portion of the pie. * **Max Context Length Error:** 11.8% (14) - Blue slice, a significant portion. * **Invalid JSON:** 2.5% (3) - Pink slice, a very small portion. * **Max Actions Error:** 5.0% (6) - A new category, represented by a dark red slice. ### Key Observations * All three models exhibit a significant proportion of "Wrong" answers, indicating a substantial error rate in the "Search and Read" task. * `ol 1 Mini` has the highest percentage of "Wrong" answers (68.1%). * `Claude 3.5 Sonnet` shows the highest percentage of "Correct" answers (37.0%) among the three models. * `LLAMA-3-170B` has the highest percentage of "Max Context Length Error" (11.8%) and introduces a new error category, "Max Actions Error" (5.0%). * "Invalid JSON" errors are relatively low for all models, except for `Claude 3.5 Sonnet` which has 9.2%. ### Interpretation The data suggests that while all three LLMs struggle with the "Search and Read" task, their error profiles differ. `ol 1 Mini` is the least accurate overall, with the highest rate of incorrect responses. `Claude 3.5 Sonnet` demonstrates the best performance in terms of correct answers, but also has a notable number of "Invalid JSON" errors. `LLAMA-3-170B` exhibits a higher rate of errors related to context length and actions, potentially indicating limitations in handling complex queries or long-form responses. The introduction of "Max Actions Error" in `LLAMA-3-170B` suggests a specific failure mode related to the model's ability to execute actions based on the search results. The differences in error types highlight the strengths and weaknesses of each model. The data could be used to inform model development efforts, focusing on addressing the specific error patterns observed for each LLM. For example, improving the context handling capabilities of `LLAMA-3-170B` or enhancing the JSON output generation of `Claude 3.5 Sonnet`. </details> Figure 11: Different technical errors for the CiteAgent with Search and Read command without Demo comparing the o1-Mini, Claude 3.5 Sonnet and LLaMA-3.1 70B backbone. <details> <summary>extracted/5974968/figures/technical_errors/error_search_only_demo2.png Details</summary>  ### Visual Description \n ## Pie Charts: Error Analysis of LLM Responses ### Overview The image presents three pie charts, each representing the error distribution for a different Large Language Model (LLM): `ol Mini`, `Claude 3.5 Sonnet`, and `LLAMA-3.1 70B`. All charts are titled "Errors [Model Name] (Search Only w/ Demo)". The charts categorize errors into "Correct", "Wrong", "Invalid JSON", and "Max Actions Error" (only present in the first chart). The data appears to represent the results of a search-only demonstration. ### Components/Axes Each chart consists of a circular pie divided into segments representing different error types. The percentage and count of each error type are displayed within each segment. There are no explicit axes, but the pie chart itself represents the proportion of each error type relative to the total number of responses. * **Chart 1 (ol Mini):** * Categories: Correct, Wrong, Invalid JSON, Max Actions Error * **Chart 2 (Claude 3.5 Sonnet):** * Categories: Correct, Wrong, Invalid JSON * **Chart 3 (LLAMA-3.1 70B):** * Categories: Correct, Wrong, Invalid JSON ### Detailed Analysis or Content Details **Chart 1: Errors ol Mini (Search Only w/ Demo)** * **Correct:** 32.8% (39) - Light Green segment, positioned at the bottom-left. * **Wrong:** 65.5% (78) - Red segment, occupying the majority of the chart, positioned at the top-right. * **Invalid JSON:** 1.0% (1) - Dark Blue segment, small segment at the top. * **Max Actions Error:** 0.6% (1) - Yellow segment, small segment at the bottom-right. **Chart 2: Errors Claude 3.5 Sonnet (Search Only w/ Demo)** * **Correct:** 43.7% (52) - Light Green segment, positioned at the bottom. * **Wrong:** 52.9% (63) - Red segment, occupying the majority of the chart, positioned at the top. * **Invalid JSON:** 3.4% (4) - Dark Blue segment, small segment at the top-left. **Chart 3: Errors LLAMA-3.1 70B (Search Only w/ Demo)** * **Correct:** 29.4% (35) - Light Green segment, positioned at the bottom-left. * **Wrong:** 56.3% (67) - Red segment, occupying the majority of the chart, positioned at the top-right. * **Invalid JSON:** 14.3% (17) - Dark Blue segment, positioned at the top. ### Key Observations * All three models exhibit a higher percentage of "Wrong" responses than "Correct" responses. * `ol Mini` has the highest percentage of "Wrong" responses (65.5%). * `Claude 3.5 Sonnet` has the highest percentage of "Correct" responses (43.7%). * `LLAMA-3.1 70B` has the highest percentage of "Invalid JSON" responses (14.3%). * `ol Mini` is the only model that exhibits "Max Actions Error". ### Interpretation The data suggests that, in this search-only demonstration, none of the LLMs consistently provide correct responses. The "Wrong" category dominates across all models, indicating a significant failure rate in generating accurate results. The presence of "Invalid JSON" errors, particularly in `LLAMA-3.1 70B`, suggests issues with the model's ability to format its output correctly. The "Max Actions Error" in `ol Mini` might indicate a limitation in the model's ability to handle complex search queries or actions. The relatively higher "Correct" response rate of `Claude 3.5 Sonnet` suggests it performs better than the other two models in this specific scenario. However, even this model still produces more "Wrong" responses than "Correct" ones. The fact that all charts are labeled "(Search Only w/ Demo)" is crucial. This implies the results are specific to a particular use case (search) and a demonstration setting, and may not generalize to other tasks or real-world applications. The demo setting may also introduce biases or limitations that affect the error rates. The counts provided alongside the percentages (e.g., 39, 63, 17) indicate the sample size for each model, which is important for assessing the statistical significance of the observed differences. </details> Figure 12: Different technical errors for the CiteAgent with Search Only command with Demo comparing the o1-Mini, Claude 3.5 Sonnet and LLaMA-3.1 70B backbone. <details> <summary>extracted/5974968/figures/technical_errors/error_search_only_no_demo2.png Details</summary>  ### Visual Description ## Pie Charts: Error Analysis of Language Models ### Overview The image presents three pie charts comparing the error types of three language models: `ol Mini`, `Claude 3.5 Sonnet`, and `LLAMA-3.1 70B`. The charts represent the distribution of errors when the models are used for search-only tasks without a demo. The error categories are "Correct", "Wrong", "Invalid JSON", and "Max Actions Error". Each pie chart includes the percentage and count of each error type. ### Components/Axes Each chart has the following components: * **Title:** Indicates the model being analyzed and the search conditions. * **Pie Segments:** Represent the proportion of each error type. * **Labels:** Each segment is labeled with the error type and the percentage/count. * **Color Coding:** * Correct: Green * Wrong: Red * Invalid JSON: Light Blue * Max Actions Error: Teal ### Detailed Analysis or Content Details **1. ol Mini (Search Only w/o Demo)** * **Correct:** 25.2% (30) - Green segment. * **Wrong:** 72.9% (86) - Red segment. * **Invalid JSON:** 1.7% (2) - Light Blue segment. * **Max Actions Error:** 0.2% (0) - Teal segment. **2. Claude 3.5 Sonnet (Search Only w/o Demo)** * **Correct:** 36.1% (43) - Green segment. * **Wrong:** 63.9% (76) - Red segment. * **Invalid JSON:** Not present. * **Max Actions Error:** Not present. **3. LLAMA-3.1 70B (Search Only w/o Demo)** * **Correct:** 29.4% (35) - Green segment. * **Wrong:** 58.0% (69) - Red segment. * **Invalid JSON:** 9.2% (11) - Light Blue segment. * **Max Actions Error:** 3.4% (4) - Teal segment. ### Key Observations * All three models have a majority of "Wrong" answers. * `ol Mini` has the lowest percentage of correct answers (25.2%) and the highest percentage of wrong answers (72.9%). * `Claude 3.5 Sonnet` has the highest percentage of correct answers (36.1%) but also a high percentage of wrong answers (63.9%). * `LLAMA-3.1 70B` shows a more diverse error distribution, with significant percentages for "Wrong", "Invalid JSON", and "Max Actions Error". * `ol Mini` is the only model that has a non-zero percentage of "Max Actions Error". * `LLAMA-3.1 70B` is the only model that has a non-negligible percentage of "Invalid JSON" errors. ### Interpretation The data suggests that all three language models struggle with accuracy in search-only tasks without a demo. The models are more likely to produce incorrect answers ("Wrong") than correct ones. The differences in error distribution between the models indicate varying strengths and weaknesses. `ol Mini` appears to be the least reliable, with a high proportion of incorrect answers and a small number of errors related to JSON formatting or action limits. `Claude 3.5 Sonnet` performs better in terms of correctness but still has a substantial error rate. `LLAMA-3.1 70B` exhibits a more complex error profile, suggesting potential issues with JSON output and action handling in addition to general inaccuracy. The presence of "Invalid JSON" and "Max Actions Error" in `LLAMA-3.1 70B` could indicate problems with the model's ability to generate well-formed JSON responses or to manage the number of actions it attempts to perform. The fact that `ol Mini` has a "Max Actions Error" suggests a similar limitation. The absence of "Invalid JSON" and "Max Actions Error" in `Claude 3.5 Sonnet` might indicate a more robust output format or better action management capabilities. However, its high "Wrong" percentage suggests that the model's core reasoning or knowledge base may be flawed. These findings highlight the importance of evaluating language models not only on overall accuracy but also on the types of errors they produce. Understanding the error distribution can help identify specific areas for improvement and guide the development of more reliable and robust language models. </details> Figure 13: Different technical errors for the CiteAgent with Search Only command without Demo comparing the o1-Mini, Claude 3.5 Sonnet and LLaMA-3.1 70B backbone. Appendix G Price and Duration Distribution In this section, we break down runtimes and costs associated with running CiteAgent with a GPT-4o or Claude 3 Opus backbone. <details> <summary>extracted/5974968/figures/price_gpt4o.png Details</summary>  ### Visual Description \n ## Histograms: Price and Duration Distribution of GPT-4o ### Overview The image presents two histograms displayed side-by-side. The left histogram shows the price distribution of GPT-4o, while the right histogram shows its duration distribution. Both histograms use frequency as the y-axis and represent the distribution of data points within defined bins. ### Components/Axes * **Left Histogram (Price Distribution):** * Title: "Price distribution of GPT-4o" * X-axis Label: "Price ($)" * Y-axis Label: "Frequency" * X-axis Scale: Ranges from approximately 0 to 4, with tick marks at 0, 1, 2, 3, and 4. * Y-axis Scale: Ranges from 0 to 50, with tick marks at intervals of 5. * **Right Histogram (Duration Distribution):** * Title: "Duration distribution of GPT-4o" * X-axis Label: "Duration (s)" * Y-axis Label: "Frequency" * X-axis Scale: Ranges from approximately 0 to 400, with tick marks at intervals of 100. * Y-axis Scale: Ranges from 0 to 35, with tick marks at intervals of 5. ### Detailed Analysis or Content Details * **Left Histogram (Price Distribution):** * The distribution is heavily skewed to the left. * The highest frequency occurs between approximately $0 and $0.5, with a frequency of around 48. * There is a significant drop in frequency as the price increases. * A secondary peak is observed between approximately $1.5 and $2, with a frequency of around 10. * There are a few data points with prices between $3 and $4, with frequencies around 2-3. * **Right Histogram (Duration Distribution):** * The distribution is also skewed to the right. * The highest frequency occurs between approximately 0 and 50 seconds, with a frequency of around 35. * The frequency decreases as the duration increases. * A secondary peak is observed between approximately 100 and 150 seconds, with a frequency of around 8. * There are a few data points with durations between 300 and 400 seconds, with frequencies around 1-2. ### Key Observations * The price of GPT-4o is predominantly low, with the majority of instances costing less than $0.50. * The duration of GPT-4o usage is also generally short, with most instances lasting less than 50 seconds. * Both distributions exhibit a long tail, indicating the presence of some instances with higher prices and longer durations. * The price distribution has a more pronounced secondary peak than the duration distribution. ### Interpretation The data suggests that GPT-4o is typically used for short, inexpensive tasks. The high frequency of low-price and short-duration instances indicates that it is well-suited for quick interactions or simple queries. The presence of longer durations and higher prices suggests that it can also be used for more complex tasks, but these are less common. The skewness of both distributions indicates that the typical use case is towards the lower end of both price and duration. The secondary peaks may represent distinct types of usage patterns or tasks. The data could be used to optimize pricing strategies or to identify areas for improving the efficiency of GPT-4o. </details> Figure 14: Price and duration distribution on CiteME with the Read and Search command with Demo for the GPT-4o backbone. The average price is $\sim$ $ $1.2$ per run or $\sim$ $ $150$ in total. The average duration is $82.9\,$ s per citation or $10772\,$ s in total. <details> <summary>extracted/5974968/figures/price_claude.png Details</summary>  ### Visual Description ## Histograms: Price and Duration Distribution of Claude Opus ### Overview The image presents two histograms displayed side-by-side. The left histogram shows the distribution of prices for Claude Opus, while the right histogram shows the distribution of durations for Claude Opus. Both histograms use the same blue color for the bars. ### Components/Axes **Left Histogram (Price Distribution):** * **Title:** "Price distribution of Claude Opus" (top-center) * **X-axis Label:** "Price ($)" (bottom-center) * **Y-axis Label:** "Frequency" (left-center) * **X-axis Scale:** Ranges from approximately 0 to 6, with markings at 1, 2, 3, 4, 5, and 6. * **Y-axis Scale:** Ranges from 0 to 25, with markings at 0, 5, 10, 15, 20, and 25. **Right Histogram (Duration Distribution):** * **Title:** "Duration distribution of Claude Opus" (top-center) * **X-axis Label:** "Duration (s)" (bottom-center) * **Y-axis Label:** "Frequency" (left-center) * **X-axis Scale:** Ranges from approximately 0 to 600, with markings at 0, 100, 200, 300, 400, 500, and 600. * **Y-axis Scale:** Ranges from 0 to 30, with markings at 0, 5, 10, 15, 20, 25, and 30. ### Detailed Analysis or Content Details **Left Histogram (Price Distribution):** The distribution is unimodal, peaking between $1 and $2. The frequency decreases as the price increases beyond $2. * Approximate bar heights (from left to right): * $0 - $0.5: 2 * $0.5 - $1: 24 * $1 - $1.5: 23 * $1.5 - $2: 18 * $2 - $2.5: 9 * $2.5 - $3: 5 * $3 - $3.5: 3 * $3.5 - $4: 2 * $4 - $4.5: 1 * $4.5 - $5: 1 * $5 - $5.5: 1 * $5.5 - $6: 1 **Right Histogram (Duration Distribution):** The distribution is unimodal, peaking between 50 and 100 seconds. The frequency decreases as the duration increases beyond 100 seconds. * Approximate bar heights (from left to right): * $0 - $50: 31 * $50 - $100: 22 * $100 - $150: 18 * $150 - $200: 8 * $200 - $250: 6 * $250 - $300: 4 * $300 - $350: 2 * $350 - $400: 2 * $400 - $450: 1 * $450 - $500: 1 * $500 - $550: 1 * $550 - $600: 1 ### Key Observations * The price distribution is heavily skewed to the left, with most prices falling below $2. * The duration distribution is also skewed to the left, with most durations falling below 150 seconds. * The price distribution has a more pronounced peak than the duration distribution. * There are very few instances of high prices (above $4) and long durations (above 400 seconds). ### Interpretation The data suggests that the majority of Claude Opus usage involves relatively low prices and short durations. This could indicate that users are primarily using the service for quick tasks or are experimenting with the platform. The skewness in both distributions suggests that there is a long tail of users who are either willing to pay more for longer usage or are experiencing longer processing times. The lack of significant frequency at the higher end of both scales suggests that these are relatively rare occurrences. The two distributions are independent, and show the price and duration of the service. The data does not suggest any correlation between price and duration. </details> Figure 15: Price and duration distribution on CiteME with the Read and Search command with Demo for the Claude Opus backbone. The average price is $\sim$ $ $1.6$ per run or $\sim$ $ $206$ in total. The average duration is $136.0\,$ s per citation or $17675\,$ s in total. <details> <summary>extracted/5974968/figures/price_o1_prewview.png Details</summary>  ### Visual Description ## Histograms: Price and Duration Distribution of o1-Preview ### Overview The image presents two histograms, side-by-side. The left histogram displays the price distribution of "o1-Preview", while the right histogram shows the duration distribution of the same. Both histograms use the same teal color for the bars. ### Components/Axes **Left Histogram (Price Distribution):** * **Title:** "Price distribution of o1-Preview" (top-center) * **X-axis Label:** "Price ($)" (bottom-center) * **Y-axis Label:** "Frequency" (left-center) * **X-axis Range:** Approximately 0.8 to 6.2 * **Y-axis Range:** Approximately 0 to 32 **Right Histogram (Duration Distribution):** * **Title:** "Duration distribution of o1-Preview" (top-center) * **X-axis Label:** "Duration (s)" (bottom-center) * **Y-axis Label:** "Frequency" (left-center) * **X-axis Range:** Approximately 0 to 4000 * **Y-axis Range:** Approximately 0 to 80 ### Detailed Analysis or Content Details **Left Histogram (Price Distribution):** The distribution is unimodal and skewed to the right. The highest frequency occurs around a price of approximately $1.8. * **Bin 1 (0.8 - 1.2):** Frequency ≈ 28 * **Bin 2 (1.2 - 1.6):** Frequency ≈ 18 * **Bin 3 (1.6 - 2.0):** Frequency ≈ 16 * **Bin 4 (2.0 - 2.4):** Frequency ≈ 10 * **Bin 5 (2.4 - 2.8):** Frequency ≈ 6 * **Bin 6 (2.8 - 3.2):** Frequency ≈ 4 * **Bin 7 (3.2 - 3.6):** Frequency ≈ 2 * **Bin 8 (3.6 - 4.0):** Frequency ≈ 1 * **Bin 9 (4.0 - 4.4):** Frequency ≈ 1 * **Bin 10 (4.4 - 4.8):** Frequency ≈ 0 * **Bin 11 (4.8 - 5.2):** Frequency ≈ 1 * **Bin 12 (5.2 - 5.6):** Frequency ≈ 0 * **Bin 13 (5.6 - 6.0):** Frequency ≈ 0 **Right Histogram (Duration Distribution):** The distribution is heavily skewed to the right. The highest frequency occurs near a duration of 0 seconds. * **Bin 1 (0 - 200):** Frequency ≈ 72 * **Bin 2 (200 - 400):** Frequency ≈ 8 * **Bin 3 (400 - 600):** Frequency ≈ 3 * **Bin 4 (600 - 800):** Frequency ≈ 2 * **Bin 5 (800 - 1000):** Frequency ≈ 1 * **Bin 6 (1000 - 1200):** Frequency ≈ 1 * **Bin 7 (1200 - 1400):** Frequency ≈ 0 * **Bin 8 (1400 - 1600):** Frequency ≈ 0 * **Bin 9 (1600 - 1800):** Frequency ≈ 0 * **Bin 10 (1800 - 2000):** Frequency ≈ 0 * **Bin 11 (2000 - 2200):** Frequency ≈ 0 * **Bin 12 (2200 - 2400):** Frequency ≈ 0 * **Bin 13 (2400 - 2600):** Frequency ≈ 0 * **Bin 14 (2600 - 2800):** Frequency ≈ 0 * **Bin 15 (2800 - 3000):** Frequency ≈ 0 * **Bin 16 (3000 - 3200):** Frequency ≈ 0 * **Bin 17 (3200 - 3400):** Frequency ≈ 0 * **Bin 18 (3400 - 3600):** Frequency ≈ 0 * **Bin 19 (3600 - 3800):** Frequency ≈ 0 * **Bin 20 (3800 - 4000):** Frequency ≈ 0 ### Key Observations * The price distribution shows a concentration of items priced around $1.8, with a long tail extending to higher prices. * The duration distribution is highly concentrated at very short durations (close to 0 seconds), with a very long tail extending to longer durations. * The vast majority of "o1-Preview" items have a duration of less than 200 seconds. ### Interpretation The data suggests that "o1-Preview" items are relatively inexpensive, with most falling below $3. However, there is a small number of items with significantly higher prices. The duration data indicates that most "o1-Preview" items are very short in duration, likely previews or short clips. The long tails in both distributions suggest the presence of outliers – a few items with very high prices and a few items with very long durations. This could indicate a variety of content types or pricing strategies within the "o1-Preview" collection. The extreme skewness in the duration distribution suggests that the majority of items are very brief, while a small number are substantially longer. This could be due to the nature of the content (e.g., short trailers vs. full-length videos). </details> Figure 16: Price and duration distribution on CiteME with the Read and Search command with Demo for the o1-Preview backbone. The average price is $\sim$ $ $1.7$ per run or $\sim$ $ $205$ in total. The average duration is $369.8\,$ s per citation or $44006\,$ s in total. <details> <summary>extracted/5974968/figures/price_o1_mini.png Details</summary>  ### Visual Description ## Histograms: Price and Duration Distribution of o1-Mini ### Overview The image presents two histograms displayed side-by-side. The left histogram shows the price distribution of "o1-Mini", while the right histogram shows its duration distribution. Both histograms use the same blue color for the bars. ### Components/Axes **Left Histogram (Price Distribution):** * **Title:** "Price distribution of o1-Mini" (top-center) * **X-axis Label:** "Price ($)" (bottom-center) * **Y-axis Label:** "Frequency" (left-center) * **X-axis Scale:** Ranges from approximately 0.0 to 3.0, with tick marks at 0.0, 0.5, 1.0, 1.5, 2.0, 2.5, and 3.0. * **Y-axis Scale:** Ranges from approximately 0 to 60, with tick marks at 0, 10, 20, 30, 40, 50, and 60. **Right Histogram (Duration Distribution):** * **Title:** "Duration distribution of o1-Mini" (top-center) * **X-axis Label:** "Duration (s)" (bottom-center) * **Y-axis Label:** "Frequency" (left-center) * **X-axis Scale:** Ranges from approximately 0 to 600, with tick marks at 0, 100, 200, 300, 400, 500, and 600. * **Y-axis Scale:** Ranges from approximately 0 to 60, with tick marks at 0, 10, 20, 30, 40, 50, and 60. ### Detailed Analysis or Content Details **Left Histogram (Price Distribution):** The distribution is heavily skewed to the right. The highest frequency occurs at a price near 0.0 ($). The frequency decreases rapidly as the price increases. * Approximate frequency at Price = 0.0 ($): 60 * Approximate frequency at Price = 0.2 ($): 23 * Approximate frequency at Price = 0.4 ($): 12 * Approximate frequency at Price = 0.6 ($): 5 * Approximate frequency at Price = 0.8 ($): 3 * Approximate frequency at Price = 1.0 ($): 2 * Approximate frequency at Price = 1.2 ($): 1 * Approximate frequency at Price = 1.4 ($): 1 * Approximate frequency at Price = 1.6 ($): 0 * Approximate frequency at Price = 1.8 ($): 0 * Approximate frequency at Price = 2.0 ($): 0 * Approximate frequency at Price = 2.2 ($): 0 * Approximate frequency at Price = 2.4 ($): 0 * Approximate frequency at Price = 2.6 ($): 0 * Approximate frequency at Price = 2.8 ($): 0 **Right Histogram (Duration Distribution):** The distribution is also skewed, but less so than the price distribution. The highest frequency occurs at a duration near 0 seconds. The frequency decreases as the duration increases, with a smaller peak around 150-200 seconds. * Approximate frequency at Duration = 0 (s): 52 * Approximate frequency at Duration = 50 (s): 20 * Approximate frequency at Duration = 100 (s): 15 * Approximate frequency at Duration = 150 (s): 12 * Approximate frequency at Duration = 200 (s): 7 * Approximate frequency at Duration = 250 (s): 4 * Approximate frequency at Duration = 300 (s): 2 * Approximate frequency at Duration = 350 (s): 1 * Approximate frequency at Duration = 400 (s): 1 * Approximate frequency at Duration = 450 (s): 0 * Approximate frequency at Duration = 500 (s): 1 * Approximate frequency at Duration = 550 (s): 0 * Approximate frequency at Duration = 600 (s): 0 ### Key Observations * The price of "o1-Mini" is heavily concentrated near 0.0 ($), with very few instances of higher prices. * The duration of "o1-Mini" is also concentrated near 0 seconds, but there is a secondary peak around 150-200 seconds, suggesting a significant number of instances with durations in that range. * Both distributions are not normally distributed. ### Interpretation The data suggests that "o1-Mini" is typically very inexpensive and has a short duration. The right skew in the price distribution indicates that while most instances are cheap, there are a few outliers with higher prices. The duration distribution suggests that most instances are very short, but a notable portion have durations between 150 and 200 seconds. This could represent different types of usage or events associated with "o1-Mini". The presence of a secondary peak in the duration distribution warrants further investigation to understand the nature of these longer-duration instances. The data could be used to understand the cost and time investment associated with using "o1-Mini". </details> Figure 17: Price and duration distribution on CiteME with the Read and Search command with Demo for the 01-Mini backbone. The average price is $\sim$ $ $0.4$ per run or $\sim$ $ $50$ in total. The average duration is $125.1\,$ s per citation or $14886\,$ s in total. <details> <summary>extracted/5974968/figures/price_claude_3.5_sonnet.png Details</summary>  ### Visual Description \n ## Histograms: Claude 3.5 Sonnet Price and Duration Distributions ### Overview The image presents two histograms displayed side-by-side. The left histogram shows the price distribution of Claude 3.5 Sonnet, while the right histogram shows its duration distribution. Both histograms use frequency as the y-axis and represent the distribution of values for the respective metrics. ### Components/Axes **Left Histogram (Price Distribution):** * **Title:** "Price distribution of Claude 3.5 Sonnet" * **X-axis Label:** "Price ($)" * **Y-axis Label:** "Frequency" * **X-axis Scale:** Ranges from approximately 0.0 to 3.0, with increments of 0.5. * **Y-axis Scale:** Ranges from 0 to 35, with increments of 5. **Right Histogram (Duration Distribution):** * **Title:** "Duration distribution of Claude 3.5 Sonnet" * **X-axis Label:** "Duration (s)" * **Y-axis Label:** "Frequency" * **X-axis Scale:** Ranges from approximately 0 to 400, with increments of 100. * **Y-axis Scale:** Ranges from 0 to 16, with increments of 2. ### Detailed Analysis or Content Details **Left Histogram (Price Distribution):** The distribution is unimodal and skewed to the right. The peak frequency occurs around a price of approximately $0.40 - $0.60, with a frequency of around 26. The frequency decreases as the price increases. * Price $0.0 - $0.2: Frequency ~ 10 * Price $0.2 - $0.4: Frequency ~ 18 * Price $0.4 - $0.6: Frequency ~ 26 * Price $0.6 - $0.8: Frequency ~ 14 * Price $0.8 - $1.0: Frequency ~ 8 * Price $1.0 - $1.2: Frequency ~ 4 * Price $1.2 - $1.4: Frequency ~ 2 * Price $1.4 - $1.6: Frequency ~ 1 * Price $1.6 - $1.8: Frequency ~ 1 * Price $1.8 - $2.0: Frequency ~ 0 * Price $2.0 - $2.2: Frequency ~ 0 * Price $2.2 - $2.4: Frequency ~ 0 * Price $2.4 - $2.6: Frequency ~ 0 * Price $2.6 - $2.8: Frequency ~ 1 * Price $2.8 - $3.0: Frequency ~ 1 **Right Histogram (Duration Distribution):** The distribution is approximately unimodal, with a peak frequency around a duration of approximately 180-200 seconds, with a frequency of around 17. The frequency decreases on both sides of the peak. * Duration 0-100s: Frequency ~ 14 * Duration 100-200s: Frequency ~ 17 * Duration 200-300s: Frequency ~ 8 * Duration 300-400s: Frequency ~ 6 ### Key Observations * The price distribution is heavily concentrated in the lower price range (below $1.00). * The duration distribution is centered around 180-200 seconds, with a significant number of responses falling within this range. * There are very few responses with prices above $2.00. * There are a small number of responses with durations exceeding 300 seconds. ### Interpretation The data suggests that the majority of Claude 3.5 Sonnet responses are relatively inexpensive (under $1.00) and take around 3-4 minutes (180-200 seconds) to generate. The right skew in the price distribution indicates that while most responses are cheap, there are a few that are significantly more expensive. The duration distribution is more symmetrical, suggesting a more consistent response time. The relatively low frequency of high-priced responses and long durations could indicate that these are either rare cases or outliers. The data could be used to understand the cost and performance characteristics of the Claude 3.5 Sonnet model. </details> Figure 18: Price and duration distribution on CiteME with the Read and Search command with Demo for the Claude 3.5 Sonnet backbone. The average price is $\sim$ $ $0.6$ per run or $\sim$ $ $80$ in total. The average duration is $143.7\,$ s per citation or $18686\,$ s in total.