2406.13975

# MR-Ben: A Meta-Reasoning Benchmark for Evaluating System-2 Thinking in LLMs > 1Chinese University of Hong Kong2University of Cambridge3University of Edinburgh4City University of Hong Kong5Tsinghua University6University of Texas at Austin7University of Hong Kong8Nanyang Technological University9Massachusetts Institute of Technology Abstract Large language models (LLMs) have shown increasing capability in problem-solving and decision-making, largely based on the step-by-step chain-of-thought reasoning processes. However, evaluating these reasoning abilities has become increasingly challenging. Existing outcome-based benchmarks are beginning to saturate, becoming less effective in tracking meaningful progress. To address this, we present a process-based benchmark MR-Ben that demands a meta-reasoning skill, where LMs are asked to locate and analyse potential errors in automatically generated reasoning steps. Our meta-reasoning paradigm is especially suited for system-2 slow thinking, mirroring the human cognitive process of carefully examining assumptions, conditions, calculations, and logic to identify mistakes. MR-Ben comprises 5,975 questions curated by human experts across a wide range of subjects, including physics, chemistry, logic, coding, and more. Through our designed metrics for assessing meta-reasoning on this benchmark, we identify interesting limitations and weaknesses of current LLMs (open-source and closed-source models). For example, with models like the o1 series from OpenAI demonstrating strong performance by effectively scrutinizing the solution space, many other state-of-the-art models fall significantly behind on MR-Ben, exposing potential shortcomings in their training strategies and inference methodologies Our dataset and codes are available on https://randolph-zeng.github.io/Mr-Ben.github.io.. footnotetext: Correspondence to: Zhijiang Guo (zg283@cam.ac.uk) and Jiaya Jia (leojia@cse.cuhk.edu.hk). 1 Introduction Reasoning, the cognitive process of using evidence, arguments, and logic to reach conclusions, is crucial for problem-solving, decision-making, and critical thinking [65, 19]. With the rapid advancement of Large Language Models (LLMs), there is an increasing interest in exploring their reasoning capabilities [30, 57]. Consequently, evaluating reasoning in LLMs reliably becomes paramount. Current evaluation methodologies primarily focus on the final result [16, 28, 22, 60], disregarding the intricacies of the reasoning process. While effective to some extent, such evaluation practices may conceal underlying issues like logical errors or unnecessary steps that compromise the accuracy and efficiency of reasoning [68, 41]. Therefore, it is important to complement outcome-based evaluation with an intrinsic evaluation of the quality of the reasoning process. However, current benchmarks for evaluating LLMs’ reasoning capabilities have certain limitations in terms of their scope and size. For instance, PRM800K [38] categorizes each reasoning step as positive, negative, or neutral. Similarly, BIG-Bench Mistake [64] focuses on identifying errors in step-level answers. We follow the same meta-reasoning paradigm as MR-GSM8K [77] and MR-Math [68], which go a step further by providing the error reason for the first negative step in the reasoning chain. However, these benchmarks are limited to a narrower task scope—MR-GSM8K and MR-Math focus solely on mathematical reasoning, while BIG-Bench Mistake mainly assesses logical reasoning. To ensure a comprehensive evaluation of reasoning abilities, it is crucial to identify reasoning errors and assess the LLMs’ capacity to elucidate them across wider domains. To bridge this gap, we construct a comprehensive benchmark MR-Ben comprising 6k questions covering a wide range of subjects, including natural sciences like math, biology, and physics, as well as coding and logic. One unique aspect of MR-Ben is its meta-reasoning paradigm, which involves challenging LLMs to reason about different forms of reasoning. In this paradigm, LLMs take on the role of a teacher, evaluating the reasoning process by assessing correctness, analyzing potential errors, and providing corrections, as depicted in Figure 1. Our analysis of various LLMs [50, 51, 5, 33, 47] uncovers distinct limitations and previously unidentified weaknesses in their reasoning abilities. While many LLMs are capable of generating correct answers, they often struggle to identify errors within their reasoning processes and explain the underlying rationale. To excel under our meta-reasoning paradigm, models must meticulously scrutinize assumptions, conditions, calculations, and logical steps, even inferring step outcomes counterfactually. These requirements align with the characteristics of “System-2” slow thinking [35, 9], which we believe remains underdeveloped in most of the state-of-the-art models we evaluated. We suspect that a key reason for this gap lies in current fine-tuning paradigms, which prioritize correct solutions and limit effective exploration of the broader solution space. Echoing this hypothesis, we observed that models like o1-preview [52], which reportedly incorporate effective search and disambiguation techniques across trajectories in the solution space, outperform other models by a large margin. Moreover, we found that leveraging high-quality and diverse synthetic data [1] significantly mitigates this issue, offering a promising path to enhance performance regardless of model size. Additionally, our results indicate that different LLMs excel in distinct reasoning paradigms, challenging the notion that domain-specific enhancements necessarily yield broad cognitive improvements. We hope that MR-Ben will guide researchers in comprehensively evaluating their models’ capabilities and foster the development of more robust AI reasoning frameworks. Our key contributions are summarized as follows: - We introduced MR-Ben, which includes around 6k questions across a wide range of subjects, from natural sciences to coding and logic, and employs a unique meta-reasoning paradigm. - We conduct an extensive analysis of various LLMs on MR-Ben, revealing various limitations and previously unidentified weaknesses in their reasoning abilities. - We offer potential pathways for enhancing the reasoning abilities of LLMs and challenge the assumption that domain-specific enhancements necessarily lead to broad improvements. <details> <summary>x1.png Details</summary>  ### Visual Description ## Reasoning Process Diagram: Arithmetic, Logical, and Algorithmic Reasoning Examples ### Overview The image presents a diagram comparing and contrasting Arithmetic, Logical, and Algorithmic Reasoning through examples. Each reasoning type is broken down into a question, a solution, and an error analysis, demonstrating a step-by-step thought process and error correction. ### Components/Axes The diagram is divided into three main columns, one for each reasoning type: 1. **Arithmetic Reasoning** (left) 2. **Logical Reasoning** (center) 3. **Algorithmic Reasoning** (right) Each column is further divided into three sections: 1. **Questions**: Poses a problem or scenario. 2. **CoT Answers (Chain of Thought)**: Shows the step-by-step solution process. 3. **Error Analysis**: Identifies and corrects errors in the initial solution. ### Detailed Analysis #### Arithmetic Reasoning * **Questions**: * Question: "Helium effuses through a pinhole 5.33 times faster than an unknown gas. That gas is most likely:" * Options: A: CO2, B: CH4, C: C5H12, D: C8H18 * **CoT Answers**: * Step 1: "Recall Graham's law of effusion..." * Step 2: "Calculate the molar masses of helium and the unknown gas..." * Step 3: "The molar mass of each candidate gas: - CO2 has a molar mass of 44 g/mol." * Step 4: "Calculate the rate ratios for each candidate gas compared to helium - Choice A: √(4/44) ≈ 0.316" * Solution: Choice A (marked with a red X indicating it's incorrect) * **Error Analysis**: * Error Step: Step 4 * Error Reason: "The ratio should be the square root of unknown gas over helium based on the Graham's law of effusion." * Correction: "Choice A: √(44/4) ≈ 3.32" #### Logical Reasoning * **Questions**: * Question: "F, G, J, K, I and M apply for a position. If interview G, interview J. If interview J, interview L. F was interviewed. F won't be interviewed unless K does. K won't be interviewed unless M does. Which of the followings might be true?" * Options: A: Only F, J and M were interviewed., B: Only F, J and K were interviewed., C: Only G and another candidate were interviewed., D: Only G and three other candidates were interviewed. * **CoT Answers**: * Step 1: "If F was interviewed, then K must have been interviewed since F will not be hired unless K is interviewed." * Step 3: "Since M was interviewed, this means that at least four candidates (F, K, M, and one more) were interviewed." * Solution: Choice D (marked with a green checkmark indicating it's correct) * **Error Analysis**: * Error Step: Step 3 * Error Reason: "If M is interviewed, it does not imply F and K are interviewed." * Correction: "There could be three possible interview combinations of candidates F, K and M, which are (M), (K, M) or (F, K, M)." #### Algorithmic Reasoning * **Questions**: * Instruction: </details> Figure 1: Overview of the evaluation paradigm and representative examples in MR-Ben. Each data point encompasses three key elements: a question, a Chain-of-Thought (CoT) answer, and an error analysis. The CoT answer is generated by various LLMs. Human experts annotate the error analyses, which include error steps, reasons behind the error, and subsequent corrections. The three examples shown are selected to represent arithmetic, logical, and algorithmic reasoning types. 2 Related Works Reasoning Benchmarks Evaluating the reasoning capabilities of LLMs is crucial for understanding their potential and limitations. While existing benchmarks often assess reasoning by measuring performance on tasks that require reasoning, such as accuracy, they often focus on specific reasoning types like arithmetic, knowledge, logic, or algorithmic reasoning. Arithmetic reasoning, involving mathematical concepts and operations, has been explored in benchmarks ranging from elementary word problems [37, 4, 55, 16] to more complex and large-scale tasks [28, 48]. Knowledge reasoning, on the other hand, requires either internal (commonsense) or external knowledge, or a combination of both [14, 62, 22]. Logical reasoning benchmarks, encompassing deductive and inductive reasoning, use synthetic rule bases for the former [15, 61, 18] and specific observations for the latter to formulate general principles [78, 71]. Algorithmic reasoning often involves understanding the coding problem description and performing multi-step reasoning to solve it [17, 25]. Benchmarks like BBH [59] and MMLU [27] indirectly assess reasoning by evaluating performance on tasks that require it. However, these benchmarks primarily focus on final results, neglecting the analysis of potential errors in the reasoning process. Unlike prior efforts, MR-Ben goes beyond accuracy by assessing the ability to locate potential errors in the reasoning process and provide explanations and corrections. Moreover, MR-Ben covers different types of reasoning, offering a more comprehensive assessment. Evaluation Beyond Accuracy Many recent studies have shifted their focus from using only the final result to evaluating the reasoning quality beyond accuracy. This shift has led to the development of two approaches: reference-free and reference-based evaluation. Reference-free methods aim to assess reasoning quality without relying on human-provided solutions. For example, ROSCOE [23] evaluates reasoning chains by quantifying reasoning errors such as redundancy and hallucination. Other approaches convert reasoning steps into structured forms, like subject-verb-object frames [56] or symbolic proofs [58], allowing for automated analysis. Reference-based methods depend on human-generated step-by-step solutions. For instance, PRM800K [38] offers solutions to MATH problems [28], categorizing each reasoning step as positive, negative, or neutral. Building on this, MR-GSM8K [77] and MR-Math [68] further provide the error reason behind the first negative step. MR-GSM8K focuses on elementary math problems, sampling questions from GSM8K [16]. MR-Math samples a smaller set of 459 questions from MATH [28]. Using the same annotation scheme, BIG-Bench Mistake [64] focuses on symbolic reasoning. It encompasses 2,186 instances from 5 tasks in BBH [59]. Despite the progress made by these datasets, limitations in scope and size remain. To address this, we introduce MR-Ben, a benchmark consisting of 5,975 manually annotated instances covering a wide range of subjects, including natural sciences, coding, and logic. MR-Ben also features more challenging questions, spanning high school, graduate, and professional levels. 3 MR-Ben: Dataset Construction 3.1 Dataset Structure To comprehensively evaluate the reasoning capabilities of LLMs, MR-Ben employs a meta-reasoning paradigm. This paradigm casts LLMs in the role of a teacher, where they assess the reasoning process by evaluating its correctness, analyzing errors, and providing corrections. As shown in Figure 1, each data point within MR-Ben consists of three key elements: a question, a CoT answer, and an error analysis. The construction pipeline is shown in Figure 6 in Appendix- D. Question The questions in MR-Ben are designed to cover a diverse range of reasoning types and difficulty levels, spanning from high school to professional levels. To ensure this breadth, we curated questions from various subjects, including natural sciences (mathematics, biology, physics), coding, and logic. Specifically, we sampled questions from mathematics, physics, biology, chemistry, and medicine from MMLU [27], which comprehensively assesses LLMs across academic and professional domains. For logic questions, we draw from LogiQA [40], which encompasses a broad spectrum of logical reasoning types, including categorical, conditional, disjunctive, and conjunctive reasoning. Finally, we select coding problems from MHPP [17], which focuses on function-level code generation requiring advanced algorithmic reasoning. Questions in MMLU and LogiQA require a single-choice answer, while MHPP requires a snippet of code as the answer. CoT Answer We queried GPT-3.5-Turbo-0125 [50], Claude2 [5], and Mistral-Medium [32] (as of February 2024) using a prompt template (provided in Figure- 7 in Appendix- D) designed to elicit step-by-step solutions [66]. For clarity, all LLMs were instructed to format their solutions with numbered steps, except for coding problems. To encourage diverse solutions, we set the temperature parameter to 1 during sampling. This empirical setting yielded satisfactory instruction following and desirable fine-grained reasoning errors, which annotators and evaluated models are expected to identify. 3.2 Annotation Process After acquiring the questions and their corresponding Chain-of-Thought (CoT) answers, we engage annotators to provide error analyses. The annotation process is divided into three stages. Answer Correctness CoT answers that result in a final answer different from the ground truth are automatically flagged as incorrect. However, for cases where the final answer matches the ground truth, manual annotation is required. This is because there are instances where the reasoning process leading to the correct answer is flawed, as illustrated in the middle example of Figure 1. Therefore, annotators are tasked with meticulously examining the entire reasoning path to determine if the correct final answer is a direct result of the reasoning process. Error Step This stage is applicable for solutions with either an unmatched final output or a matched final output underpinned by flawed reasoning. Following the prior effort [38], each step in the reasoning process is categorized as positive, neutral, or negative. Positive and neutral steps represent stages where the correct final output remains attainable. Conversely, negative steps indicate a divergence from the path leading to the correct solution. Annotators are required to identify the first step in the reasoning process where the conditions, assumptions, or calculations are incorrect, making the correct final result unreachable for the subsequent reasoning steps. Error Reason and Correction Annotators are tasked with conducting an in-depth analysis of the reasoning that led to the identified error. As shown in Figure 1, annotators are required to provide the error reason and the corresponding correction to this reasoning step. This comprehensive approach ensures a thorough understanding and rectification of errors in the reasoning process. 3.3 Data Statistics Table 1: Statistics of MR-Ben. The length of questions and solutions are measured inthe number of words. Notice that the steps for coding denote the number of lines of code. They are not directly comparable with other subjects. | Correct Solution Ratio Avg Solution Steps Avg First Error Step | 16.2% 6.8 3.1 | 31.0% 5.3 3.0 | 59.6% 5.1 2.7 | 47.8% 5.7 3.1 | 45.0% 5.6 3.0 | 51.1% 5.3 2.8 | 31.1% 32.5* 14.0* | 40.3% 9.5 4.5 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | | Avg Length of Questions | 44.3 | 88.7 | 56.3 | 66.6 | 48.1 | 154.8 | 140.1 | 85.6 | | Avg Length of Solutions | 205.9 | 206.1 | 187.6 | 199.4 | 194.5 | 217.7 | 950.3 | 308.8 | Table 1 presents the statistics of MR-Ben. The benchmark exhibits a balanced distribution of correct and incorrect solutions, with an overall correct solution rate of 40.3%. Solutions, on average, involve 9.5 steps, and errors typically manifest around the fourth step (4.5). The questions and solutions are substantial, with average lengths of 85.6 and 308.8 words, respectively. The subject-wise analysis reveals that Math is the most challenging, with a correct solution rate of a mere 16.2%. This could be attributable to the intricacy of the arithmetic operations involved. Conversely, Biology emerges as the least daunting, with a high correct solution rate of 59.6%. Coding problems have the longest solutions, averaging 950.3 number of words. This underscores the complexity and the detailed procedural reasoning inherent in coding tasks. Similarly, Logic problems have the longest questions, averaging 154.8 words. This is in line with the need for elaborate descriptions in logical reasoning. The typical step at which the first error occurs is fairly consistent across most subjects, usually around the 3rd step out of a total of 5. However, Coding deviates from this trend. The first error tends to appear earlier, specifically around the 14th line out of a total of 32.5 lines. This suggests that the problem-solving process in Coding may have distinct dynamics compared to other subjects. 3.4 Quality Control Annotators Given the complexity of the questions, which span a range of subjects from high school to professional levels, we enlisted the services of an annotation company. This company meticulously recruited annotators, each holding a minimum of a bachelor’s degree. Before their trial labeling, annotators are thoroughly trained and are required to review the annotation guidelines. We’ve included the guidelines for all subjects in Appendix H for reference. The selection of annotators is based on their performance on a balanced, small hold-out set of problems for each subject. In addition to the annotators, a team of 14 quality controllers diligently monitors the quality of the annotation weekly. As a final layer of assurance, we have 4 meta controllers who scrutinize the quality of the work. Quality Assurance Every problem in MR-Ben undergoes a rigorous three-round quality assurance process to ensure its accuracy and clarity. Initially, each question is labeled by two different annotators. Any inconsistencies in the solution correctness or the first error step are identified and reviewed by a quality controller for arbitration. Following this, every annotated problem is subjected to a secondary review by annotators who were not involved in the initial labeling. This is to ensure that the annotations for different solutions to the same problem are consistent and coherent. In the final phase of the review, 10% of the problems are randomly sampled and reviewed by the meta controllers. Throughout the entire evaluation process, all annotated fields are meticulously examined in multiple rounds for their accuracy and clarity. Any incorrect annotations or those with disagreements are progressively filtered out and rectified, ensuring a high-quality dataset. This rigorous process allows us to maintain a high level of annotation quality. Dataset Artifacts & Biases Table 1 reveals a relatively balanced distribution of correct and incorrect solutions. However, an exception was observed in mathematical subjects, where the distribution tends to skew towards incorrect solutions. This skew could suggest an inherent complexity or ambiguity in mathematical problem statements. Our analysis of the first error step across all subjects indicated that errors predominantly occur in the initial stages ( $n≤ 7$ ) of problem-solving and are distributed relatively uniformly. This pattern was consistent across most subjects, with no significant skew towards later steps. More detailed discussions of biases are provided in the Appendix C. 4 Evaluation For each question-solution pair annotated, the evaluated model are supposed to decide the correctness of the solution and report the first-error-step and error-reason if any. The solution-correctness and first-error-step is scored automatically based on the manual annotation result. Only when the evaluated model correctly identified the incorrect solution and first-error-step will its error-reason be further examined manually or automatically by models. Therefore in order to provide a unified and normalized score to reflect the overall competence of the evaluated model, we follow the work of [77] and apply a metric named MR-Score, which consist of three sub-metrics. The first one is the Matthews Correlation Coefficient (a.k.a MCC, 46) for the binary classification of solution-correctness. $$ \displaystyle MCC=\frac{TP\times TN-FP\times FN}{\sqrt{(TP+FP)\times(TP+FN)% \times(TN+FP)\times(TN+FN)}} \tag{1} $$ where TP, TN, FP, FN stand for true positive, true negative, false positive and false negative. The MCC score ranges from -1 to +1 with -1 means total disagreement between prediction and observation, 0 indicates near random performance and +1 represents perfect prediction. In the context of this paper, we interpret negative values as no better than random guess and set 0 as cut-off threshold for normalization purpose. The second metric is the ratio between numbers of solutions with correct first-error-step predicted and the total number of incorrect solutions. $$ \begin{split}ACC_{\text{step}}=\frac{N_{\text{correct\_first\_error\_step}}}{N% _{\text{incorrect\_sols}}}\end{split} \tag{2} $$ The third metrics is likewise the ratio between number of solutions with correct first-error-step plus correct error-reason predicted and the total number of incorrect solutions. $$ \begin{split}ACC_{\text{reason}}=\frac{N_{\text{correct\_error\_reason}}}{N_{% \text{incorrect\_sols}}}\end{split} \tag{3} $$ MR-Score is then a weighted combination of three metrics, given by $$ \displaystyle MR\mbox{-}Score \displaystyle=w_{1}*\max(0,MCC)+w_{2}*ACC_{\text{step}}+w_{3}*ACC_{\text{% reason}} \tag{4} $$ For the weights $w_{1},w_{2}$ and $w_{3}$ , they are chosen based on our evaluation results to maximize the differentiation between different models. It is important to note that the Matthews Correlation Coefficient (MCC) and the accuracy of locating the first error step can be directly calculated by comparing the responses of the evaluated model with the ground truth annotations. However, assessing the accuracy of the error reason explained by the evaluated model presents more complexity. While consulting domain experts for annotations is a feasible approach, we instead utilized GPT-4-Turbo as a proxy to examine the error reasons, as detailed in Figure- 11 in Appendix- D. We operate under the assumption that while our benchmark presents a significant challenge for GPT-4 in evaluating complete solution correctness—identifying the first error step and explaining the error reason—it is comparatively easier for GPT-4 to assess whether the provided error reasons align with the ground truth. Specifically, in a hold-out set of sampled error reasons, there was a 92% agreement rate between the manual annotations by the authors and those generated by GPT-4. For more detailed evaluations on the robustness of MR-Score and its design thinking, please refer to our discussion in Appendix- B. 5 Experiments 5.1 Experiment Setup Table 2: Evaluation results on MR-Ben: This table presents a detailed breakdown of each model’s performance evaluated under metric MR-Score across different subjects, where K stands for the number of demo examples here. | Model | Bio. | | Phy. | | Math | | Chem. | | Med. | | Logic | | Coding | | Avg. | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | | Closed-Source LLMs | | | | | | | | | | | | | | | | | | | | | | | | | Claude3-Haiku | 5.7 | 5.8 | | 3.3 | 3.5 | | 3.1 | 3.1 | | 6.5 | 6.4 | | 2.0 | 2.0 | | 1.2 | 1.2 | | 9.0 | 0.0 | | 4.4 | 3.1 | | GPT-3.5-Turbo | 3.6 | 6.6 | | 5.7 | 6.7 | | 5.7 | 5.4 | | 4.9 | 6.7 | | 3.6 | 4.4 | | 1.7 | 4.5 | | 3.0 | 4.1 | | 4.0 | 5.5 | | Doubao-pro-4k | 8.4 | 13.5 | | 10.0 | 11.7 | | 12.3 | 15.5 | | 10.6 | 17.5 | | 5.9 | 10.0 | | 4.5 | 5.5 | | 9.8 | 7.4 | | 8.8 | 11.6 | | Mistral-Large | 22.2 | 28.0 | | 26.7 | 25.4 | | 24.3 | 28.2 | | 24.0 | 27.0 | | 15.9 | 19.3 | | 14.7 | 17.1 | | 21.1 | 21.4 | | 21.3 | 23.8 | | Yi-Large | 35.3. | 40.7 | | 37.2 | 36.8 | | 36.5 | 20.6 | | 40.0 | 39.1 | | 29.3 | 32.1 | | 25.1 | 31.3 | | 21.9 | 25.7 | | 32.2 | 32.3 | | Moonshot-v1-8k | 35.0 | 36.8 | | 33.8 | 33.8 | | 34.9 | 33.0 | | 36.7 | 35.0 | | 29.4 | 32.3 | | 25.0 | 29.2 | | 32.7 | 31.2 | | 32.5 | 33.0 | | GPT-4o-mini | 37.7 | 38.9 | | 38.5 | 37.4 | | 44.4 | 40.4 | | 39.2 | 37.0 | | 33.9 | 25.1 | | 23.6 | 17.7 | | 41.6 | 34.9 | | 37.0 | 33.1 | | Zhipu-GLM-4 | 40.7 | 46.2 | | 37.7 | 42.5 | | 38.4 | 36.6 | | 43.1 | 44.0 | | 34.5 | 41.0 | | 37.5 | 32.5 | | 38.8 | 32.8 | | 38.7 | 39.4 | | GPT-4-Turbo | 44.7 | 47.3 | | 42.8 | 45.2 | | 44.3 | 45.4 | | 44.0 | 46.0 | | 38.8 | 38.4 | | 34.1 | 33.6 | | 53.6 | 57.3 | | 43.2 | 44.7 | | GPT-4o | 48.3 | 49.1 | | 45.5 | 48.2 | | 42.6 | 41.3 | | 48.2 | 49.1 | | 47.9 | 47.7 | | 31.9 | 28.4 | | 56.5 | 54.6 | | 45.8 | 45.5 | | o1-mini | 45.8 | 46.9 | | 56.0 | 53.8 | | 68.5 | 67.0 | | 55.2 | 56.1 | | 45.9 | 47.2 | | 30.7 | 28.7 | | 55.1 | 55.6 | | 51.0 | 50.8 | | o1-preview | 54.1 | 56.0 | | 62.2 | 61.7 | | 69.8 | 70.3 | | 60.6 | 60.3 | | 54.3 | 55.1 | | 46.1 | 45.3 | | 65.1 | 70.0 | | 58.9 | 59.8 | | Open-Source Small | | | | | | | | | | | | | | | | | | | | | | | | | Qwen1.5-1.8B | 0.0 | 0.0 | | 0.0 | 0.0 | | 0.0 | 0.1 | | 0.0 | 0.1 | | 0.0 | 0.0 | | 0.0 | 0.1 | | 0.0 | 0.0 | | 0.0 | 0.0 | | Gemma-2B | 0.1 | 0.0 | | 0.0 | 0.0 | | 0.0 | 1.0 | | 0.1 | 0.0 | | 0.0 | 0.4 | | 0.0 | 0.2 | | 0.7 | 0.0 | | 0.1 | 0.2 | | Qwen2-1.5B | 2.2 | 2.8 | | 2.2 | 1.3 | | 3.3 | 6.3 | | 2.5 | 3.3 | | 2.9 | 11.2 | | 1.5 | 9.4 | | 0.0 | 3.6 | | 2.1 | 5.4 | | Phi3-3.8B | 13.4 | 12.5 | | 12.7 | 10.8 | | 13.3 | 13.1 | | 16.4 | 17.1 | | 10.2 | 8.1 | | 8.4 | 5.3 | | 9.1 | 10.2 | | 11.9 | 11.0 | | Open-Source LLMs Medium | | | | | | | | | | | | | | | | | | | | | | | | | GLM-4-9B | 4.4 | 2.4 | | 9.6 | 1.2 | | 8.1 | 4.7 | | 8.7 | 2.9 | | 2.3 | 1.9 | | 2.5 | 1.6 | | 11.4 | 0.0 | | 6.7 | 2.1 | | DeepSeek-7B | 5.7 | 6.2 | | 4.7 | 2.6 | | 4.9 | 5.2 | | 4.2 | 4.9 | | 3.1 | 1.6 | | 3.0 | 3.8 | | 0.0 | 1.2 | | 3.7 | 3.6 | | Deepseek-Coder-33B | 7.4 | 5.5 | | 7.8 | 5.6 | | 7.2 | 8.6 | | 7.8 | 7.4 | | 6.0 | 5.5 | | 4.6 | 6.7 | | 8.4 | 4.9 | | 7.0 | 6.3 | | DeepSeek-Coder-7B | 10.5 | 9.9 | | 11.8 | 9.6 | | 11.8 | 12.1 | | 12.3 | 11.9 | | 10.4 | 11.0 | | 9.8 | 10.7 | | 5.0 | 5.8 | | 10.2 | 10.2 | | LLaMA3-8B | 12.0 | 11.9 | | 10.9 | 7.5 | | 15.0 | 9.0 | | 12.6 | 12.7 | | 9.3 | 8.0 | | 9.4 | 9.6 | | 15.8 | 10.0 | | 12.2 | 9.8 | | Yi-1.5-9B | 10.4 | 14.8 | | 11.9 | 12.9 | | 12.5 | 15.6 | | 13.1 | 14.4 | | 9.5 | 14.8 | | 9.1 | 9.5 | | 4.8 | 6.3 | | 10.2 | 12.6 | | Open-Source LLMs Large | | | | | | | | | | | | | | | | | | | | | | | | | Qwen1.5-72B | 15.3 | 19.2 | | 12.9 | 13.6 | | 12.0 | 10.0 | | 13.9 | 16.3 | | 11.7 | 14.7 | | 10.4 | 12.9 | | 3.9 | 5.9 | | 11.5 | 13.3 | | DeepSeek-67B | 17.1 | 19.7 | | 14.9 | 17.3 | | 15.4 | 16.2 | | 16.3 | 20.6 | | 14.7 | 12.2 | | 13.6 | 14.3 | | 14.5 | 15.2 | | 15.2 | 16.5 | | LLaMA3-70B | 20.4 | 27.1 | | 17.4 | 20.5 | | 14.9 | 15.8 | | 19.5 | 25.1 | | 16.3 | 19.3 | | 16.3 | 16.8 | | 29.8 | 16.7 | | 19.2 | 20.2 | | DeepSeek-V2-236B | 30.0 | 37.1 | | 32.2 | 36.5 | | 32.2 | 30.0 | | 32.5 | 35.4 | | 26.5 | 32.4 | | 23.6 | 27.4 | | 34.2 | 27.1 | | 30.2 | 32.3 | | Qwen2-72B | 36.0 | 40.8 | | 36.7 | 40.9 | | 38.0 | 38.7 | | 37.2 | 38.8 | | 28.3 | 29.3 | | 25.6 | 20.5 | | 31.3 | 30.4 | | 33.3 | 34.2 | To evaluate the performance of different models on our new benchmark, we selected a diverse array of models based on size and source accessibility Note: All models used in our experiments are instruction-finetuned versions, although this is not indicated in their abbreviated names. This included smaller models like Gemma-2B [63], Phi-3 [1], Qwen1.5-1.8B [7], as well as larger counterparts such as Llama3-70B [47], Deepseek-67B [10], and Qwen1.5-72B [7]. We also compared open-source models (e.g. models from the Llama3 and Qwen1.5/Qwen2 series) against closed-source models from the GPT [51], Claude [6], Mistral [32], GLM [3], Yi [39], Moonshot [2], Doubao [12] families. Additionally, models from the Deepseek-Coder [10] series were included to assess the impact of coding-focused pretraining on reasoning performance. Given the complexity of our benchmark, even larger open-source models like Llama3-70B-Instruct struggle to produce accurate evaluation results without the use of prompting methods, often achieving MR-Scores near zero. Consequently, we employed a step-wise chain-of-thought prompting technique similar to those described in [77, 64]. This approach guides models in systematically reasoning through solution traces before making final decisions, as detailed in Appendix- D. Considering the complexity of the task, which includes question comprehension, reasoning through the provided solutions, and adhering to format constraints, few-shot demonstration setups are also explored to investigate if models can benefit from In-Context Learning (ICL) examples. Due to the context token limits, we report zero and one-shot results in the main result table (Table 2) For the breakdown performances of models in the sub-tasks, please refer to Table 7. The performance of additional few-shot configurations on a selection of models with various capabilities is further discussed in Section 6.1. <details> <summary>x2.png Details</summary>  ### Visual Description ## Radar Chart: Model Performance Across Disciplines ### Overview The image is a radar chart comparing the performance of five different models (DeepSeek-v2, GPT-4-turbo, O1-Preview, Qwen2-72B, and GLM-4) across seven disciplines: math, chemistry, biology, logic, coding, medicine, and physics. The chart visualizes the relative strengths and weaknesses of each model in these areas. ### Components/Axes * **Axes:** The chart has seven radial axes, each representing a different discipline: math, chemistry, biology, logic, coding, medicine, and physics. * **Scale:** The radial scale ranges from 0 to 0.7, with increments of 0.1. * **Models (Legend):** * DeepSeek-v2 (brown) * GPT-4-turbo (blue) * O1-Preview (light green) * Qwen2-72B (green) * GLM-4 (light red) ### Detailed Analysis Here's a breakdown of each model's performance in each discipline: * **DeepSeek-v2 (brown):** * math: ~0.45 * chemistry: ~0.45 * biology: ~0.45 * logic: ~0.45 * coding: ~0.45 * medicine: ~0.45 * physics: ~0.45 * Trend: Relatively consistent performance across all disciplines. * **GPT-4-turbo (blue):** * math: ~0.3 * chemistry: ~0.3 * biology: ~0.3 * logic: ~0.3 * coding: ~0.1 * medicine: ~0.2 * physics: ~0.3 * Trend: Relatively consistent performance across most disciplines, with a dip in coding and medicine. * **O1-Preview (light green):** * math: ~0.65 * chemistry: ~0.7 * biology: ~0.7 * logic: ~0.7 * coding: ~0.7 * medicine: ~0.7 * physics: ~0.7 * Trend: Consistently high performance across all disciplines. * **Qwen2-72B (green):** * math: ~0.35 * chemistry: ~0.4 * biology: ~0.4 * logic: ~0.4 * coding: ~0.5 * medicine: ~0.4 * physics: ~0.4 * Trend: Relatively consistent performance across most disciplines, with a slight increase in coding. * **GLM-4 (light red):** * math: ~0.3 * chemistry: ~0.3 * biology: ~0.3 * logic: ~0.3 * coding: ~0.3 * medicine: ~0.3 * physics: ~0.3 * Trend: Consistent performance across all disciplines. ### Key Observations * O1-Preview (light green) consistently outperforms the other models across all disciplines. * DeepSeek-v2 (brown) shows relatively consistent performance across all disciplines, but at a lower level than O1-Preview. * GPT-4-turbo (blue) has a noticeable dip in performance in coding compared to other disciplines. * GLM-4 (light red) shows consistent performance across all disciplines. * Qwen2-72B (green) shows a slight increase in coding performance compared to other disciplines. ### Interpretation The radar chart provides a visual comparison of the strengths and weaknesses of different models across various academic disciplines. O1-Preview appears to be the most versatile and high-performing model, while the other models exhibit varying degrees of specialization or consistent performance at lower levels. The chart highlights the importance of considering specific domain requirements when selecting a model, as some models may excel in certain areas while underperforming in others. The consistent performance of DeepSeek-v2 and GLM-4 suggests a balanced approach, while the dip in GPT-4-turbo's coding performance indicates a potential area for improvement or a trade-off in its design. </details> Figure 2: Model performance across subjects <details> <summary>x3.png Details</summary>  ### Visual Description ## Bar Chart: MR-Scores of Models on Different Reasoning Paradigms ### Overview The image is a bar chart comparing the MR-Scores of different models (DeepSeek-v2, GPT-4-turbo, O1-Preview, Qwen2-72B, GLM-4) across four reasoning paradigms: knowledge, logic, arithmetic, and algorithmic. The chart displays the MR-Scores on the y-axis and the models on the x-axis. Each model has four bars representing its performance in each of the four paradigms. ### Components/Axes * **Title:** MR-Scores of Models on Different Reasoning Paradigms * **X-axis:** * **Label:** Models * **Categories:** DeepSeek-v2, GPT-4-turbo, O1-Preview, Qwen2-72B, GLM-4 * **Y-axis:** * **Label:** MR-Scores * **Scale:** 0.0 to 0.65, with increments of 0.1 * **Legend (top-right):** * **Title:** Paradigms * **Colors and Labels:** * Light Blue: knowledge * Dark Blue: logic * Light Green: arithmetic * Dark Green: algorithmic * **Horizontal Gridlines:** Present at each 0.1 increment on the y-axis. * **Horizontal Dashed Line:** Present at y=0.5 ### Detailed Analysis **DeepSeek-v2:** * Knowledge (Light Blue): ~0.32 * Logic (Dark Blue): ~0.30 * Arithmetic (Light Green): ~0.40 * Algorithmic (Dark Green): ~0.42 **GPT-4-turbo:** * Knowledge (Light Blue): ~0.49 * Logic (Dark Blue): ~0.36 * Arithmetic (Light Green): ~0.47 * Algorithmic (Dark Green): ~0.50 **O1-Preview:** * Knowledge (Light Blue): ~0.56 * Logic (Dark Blue): ~0.46 * Arithmetic (Light Green): ~0.66 * Algorithmic (Dark Green): ~0.65 **Qwen2-72B:** * Knowledge (Light Blue): ~0.34 * Logic (Dark Blue): ~0.26 * Arithmetic (Light Green): ~0.37 * Algorithmic (Dark Green): ~0.31 **GLM-4:** * Knowledge (Light Blue): ~0.39 * Logic (Dark Blue): ~0.37 * Arithmetic (Light Green): ~0.38 * Algorithmic (Dark Green): ~0.39 ### Key Observations * O1-Preview has the highest MR-Scores overall, particularly in arithmetic and algorithmic reasoning. * Qwen2-72B generally has the lowest MR-Scores across all paradigms. * For most models, the algorithmic reasoning score is either the highest or close to the highest. * The horizontal dashed line at MR-Score = 0.5 serves as a visual reference point. ### Interpretation The bar chart provides a comparative analysis of the performance of different models on various reasoning paradigms. The data suggests that the O1-Preview model excels in both arithmetic and algorithmic reasoning compared to the other models. Qwen2-72B appears to be the weakest performer across all paradigms. The proximity of arithmetic and algorithmic scores for most models may indicate a correlation between these two types of reasoning tasks. The dashed line at 0.5 helps to quickly assess which models and paradigms achieve a relatively high MR-Score. </details> Figure 3: Model performance on different reasoning paradigms 5.2 Experiment Results The MR-Ben benchmark presents a significant shift in the challenge for state-of-the-art large language models, transitioning from question-answering to the nuanced role of question-solution scoring. This section details our findings, emphasizing variations in model performances and their implications. Overall Performance Among the evaluated models, o1-preview consistently achieves the highest MR-Scores across all subjects, significantly outperforming most competitors from both open and closed-source communities. Notably, the open-sourced Qwen2-72B and Deepseek-V2-236B models are performing exceptionally well, surpassing every other open-sourced model including Llama3 by a large margin. Their scores are even comparable to or greater than some of the most capable models from commercial companies, such as Mistral, Yi, and Moonshot AI. In the small language model category, the performance of Phi3-3.8B exceeds many of the mid-size models, including Deepseek-Coder-33B, whose size is around tenfold larger. Performance across Model Size and Reasoning Paradigm Table 2 reveals a general trend where larger models tend to perform better, highlighting the correlation between model size and the efficacy in complex reasoning tasks. However, this relationship is not strictly linear, as demonstrated by models like Phi3-3.8B, which excel despite their smaller size. Since MR-Ben challenges the language models to reason about the reasoning in the solution space among a diverse range of domains, models like Phi-3 that are trained with effective data synthesis techniques and broader coverage of the solution space, intuitively achieve higher MR-Score. This suggests that while larger model sizes generally yield superior performances, techniques like knowledge distillation can also significantly boost reasoning performance. Similarly, although the size of the o1 model series remains undisclosed, these models reportedly employ mechanisms that scale computation efficiently through effective exploration, frequent retrospection, and meticulous reflection within the solution space. These characteristics align closely with the principles of “system-2” thinking, which emphasizes deliberate, reflective problem-solving. As a result, the o1 models demonstrate a more effective reasoning process, achieving significantly higher MR-Scores than other models by a large margin. Performance across Reasoning Types Our categorization into four reasoning types—knowledge, arithmetic, algorithmic, and logic—illustrates the unique challenges each model faces within these paradigms (Figure 3). Logic reasoning emerges as the most formidable due to the intricate logical operations required by questions from the LogiQA dataset. In stark contrast, o1-Preview and GPT-4-turbo demonstrate exceptional prowess in algorithmic reasoning, where their capabilities markedly surpass other models. Notably, models excel in different reasoning paradigms, reflecting their varied strengths and training backgrounds. For instance, despite Deepseek-Coder’s specialized pre-training focused on coding tasks, it does not necessarily confer superior abilities in algorithmic reasoning, underscoring that targeted pretraining does not guarantee enhanced performance across all reasoning types. Comparing the performance of the Deepseek-Coder with that of the Phi-3 model, which excels despite its much smaller size, highlights the potential significance of high-quality synthetic data in achieving broad-based reasoning capabilities. Sensitivity to Task Difficulty and Solution Length An examination across educational levels shows most models perform better at high school-level questions than college-level ones, indicating an intuitive level of sensitivity to the difficulty levels of the questions. Additionally, our analysis finds a minor negative correlation between the length of solution steps and MR-Scores, as detailed in Figure 5 and Figure 5. Summary: MR-Ben effectively differentiates model capabilities, often obscured in simpler settings. It not only identifies top performers but also underscores the influence of model size on outcomes, while demonstrating that techniques like knowledge distillation and test-time compute scaling, as seen with the Phi-3 and o1 models, can notably enhance smaller models’ performance, challenging the dominance of larger models. The analysis further reveals that specialized training, such as in coding, does not guarantee superior algorithmic reasoning. This suggests the potential need for more balanced data approaches or improved data synthesis methods. 6 Further Analysis & Discussion <details> <summary>x4.png Details</summary>  ### Visual Description ## Bar Chart: MR-Scores of Models on Different Difficulty Levels ### Overview The image is a bar chart comparing the MR-Scores of different models on two difficulty levels: high school and college. The chart displays the MR-Scores for each model across the two difficulty levels, allowing for a direct comparison of performance. ### Components/Axes * **Title:** MR-Scores of Models on Different Difficulty Levels * **X-axis:** Models (DeepSeek-v2, GPT-4-turbo, O1-Preview, Qwen2-72B, GLM-4) * **Y-axis:** MR-Scores, ranging from 0.0 to 0.6 in increments of 0.1. * **Legend:** Located in the top-right corner, indicating: * Light Blue: high\_school * Dark Blue: college * A horizontal dashed line is present at MR-Score = 0.5 ### Detailed Analysis Here's a breakdown of the MR-Scores for each model and difficulty level: * **DeepSeek-v2:** * high\_school: ~0.37 * college: ~0.29 * **GPT-4-turbo:** * high\_school: ~0.50 * college: ~0.38 * **O1-Preview:** * high\_school: ~0.62 * college: ~0.57 * **Qwen2-72B:** * high\_school: ~0.37 * college: ~0.34 * **GLM-4:** * high\_school: ~0.38 * college: ~0.40 ### Key Observations * O1-Preview has the highest MR-Scores for both high school and college difficulty levels. * DeepSeek-v2 and Qwen2-72B have the lowest MR-Scores for both difficulty levels. * For most models, the MR-Score is higher for the high school difficulty level compared to the college difficulty level, except for GLM-4. ### Interpretation The bar chart provides a comparative analysis of the performance of different models on varying difficulty levels, as measured by MR-Scores. The data suggests that O1-Preview is the most effective model among those tested, achieving the highest MR-Scores for both high school and college difficulty levels. Conversely, DeepSeek-v2 and Qwen2-72B appear to be the least effective, with the lowest MR-Scores. The fact that most models perform better on high school level questions suggests that the college level questions are more challenging. The exception to this trend is GLM-4, which performs slightly better on college level questions. </details> Figure 4: MR-Scores of different models on different levels of difficulty <details> <summary>x5.png Details</summary>  ### Visual Description ## Line Chart: Impact of Solution Length on MR-Scores by Models ### Overview The image is a line chart comparing the MR-Scores of different models (DeepSeek-v2, GPT-4-turbo, O1-Preview, Qwen2-72B, and GLM-4) across varying solution step numbers. The x-axis represents the solution step numbers, and the y-axis represents the MR Score. ### Components/Axes * **Title:** Impact of Solution Length on MR-Scores by Models * **X-axis:** Solution Step Numbers, with markers at "<=5", "6", "7", "8", "9", "10", "11", "12", ">=13" * **Y-axis:** MR Score, with markers from 0.0 to 0.7 in increments of 0.1. * **Legend:** Located in the bottom-right corner, it identifies each model by color: * Blue: DeepSeek-v2 * Orange: GPT-4-turbo * Green: O1-Preview * Red: Qwen2-72B * Purple: GLM-4 ### Detailed Analysis * **DeepSeek-v2 (Blue):** * Trend: Initially increases, then decreases sharply, and finally increases again. * Data Points: * <=5: ~0.29 * 6: ~0.34 * 7: ~0.38 * 8: ~0.30 * 9: ~0.50 * 10: ~0.21 * 11: ~0.10 * 12: ~0.25 * >=13: ~0.43 * **GPT-4-turbo (Orange):** * Trend: Increases, then decreases, and increases again. * Data Points: * <=5: ~0.39 * 6: ~0.47 * 7: ~0.41 * 8: ~0.44 * 9: ~0.56 * 10: ~0.27 * 11: ~0.48 * 12: ~0.25 * >=13: ~0.38 * **O1-Preview (Green):** * Trend: Relatively stable, with a slight increase and then a decrease. * Data Points: * <=5: ~0.55 * 6: ~0.56 * 7: ~0.58 * 8: ~0.60 * 9: ~0.60 * 10: ~0.62 * 11: ~0.58 * 12: ~0.50 * >=13: ~0.49 * **Qwen2-72B (Red):** * Trend: Relatively stable, with a slight decrease and then a slight increase. * Data Points: * <=5: ~0.33 * 6: ~0.38 * 7: ~0.30 * 8: ~0.31 * 9: ~0.38 * 10: ~0.32 * 11: ~0.25 * 12: ~0.25 * >=13: ~0.31 * **GLM-4 (Purple):** * Trend: Fluctuates, with an initial increase, then a decrease, and then another increase. * Data Points: * <=5: ~0.38 * 6: ~0.40 * 7: ~0.34 * 8: ~0.36 * 9: ~0.38 * 10: ~0.34 * 11: ~0.39 * 12: ~0.25 * >=13: ~0.36 ### Key Observations * O1-Preview consistently has the highest MR Score across all solution step numbers. * DeepSeek-v2 shows the most significant fluctuation in MR Score, with a sharp drop at solution step number 11. * GPT-4-turbo also shows significant fluctuation, with a peak at step number 9. * Qwen2-72B has the most stable MR Score across all solution step numbers. * GLM-4's performance fluctuates, but not as dramatically as DeepSeek-v2 or GPT-4-turbo. ### Interpretation The chart illustrates how the length of the solution (measured by the number of steps) impacts the performance (MR-Score) of different models. O1-Preview appears to be the most robust model, maintaining a relatively high MR-Score regardless of the solution length. DeepSeek-v2 and GPT-4-turbo are more sensitive to solution length, with their performance varying significantly. The data suggests that the optimal solution length may differ for each model, and some models are more consistent in their performance than others. The sharp drop in DeepSeek-v2's performance at step number 11 could indicate a specific weakness or limitation of that model when dealing with solutions of that length. </details> Figure 5: The MR-Scores of models on solutions with different step numbers. 6.1 Few Shot Prompting As previously discussed and exemplified by our prompt template (Figure 10 in Appendix- D), our evaluation method is characterized by its high level of difficulty and complexity. In this experiment, we aimed to determine whether providing a few step-wise chain-of-thought (CoT) examples could improve model performance in terms of format adherence and reasoning quality. The results, as presented in Table 9 in Appendix, do not show a consistent pattern as the number of shots increases. While smaller language models like Gemma-2B exhibit performance improvements with additional shots, the performance of larger language models tends to fluctuate with an increasing number of shots. We hypothesize that for our complex tasks, the lengthy few-shot demonstrations may act more as a hindrance, providing distracting information rather than aiding in format adherence and reasoning. Our empirical findings suggest that a one-shot demonstration strikes the optimal balance between providing guidance and minimizing distraction. This supports our decision to focus on zero-shot versus one-shot comparisons in our primary experiments, as detailed in Table 2. 6.2 Self Refine Prompting As suggested by [31], large language models typically cannot perform self-correction without external ground truth feedback. To explore whether this phenomenon occurs in our benchmark, we adopted a similar setting by prompting the language model to verify its own answer across a three-round interaction sequence: query, examine, and refine. Our prompting template, detailed in Figure 8 in Appendix D, is minimalistic and designed solely to encourage the model to self-examine. The results of this self-refinement process are recorded in Table 4. Notably, models smaller than Llama3-70B exhibit performance degradation with self-refinement, while larger models, such as GPT-4, show marginal benefits from the process. Conversely, from Llama3-8B to Llama3-70B, despite a significant portion of correct predictions shifting to incorrect ones, as previously reported by [31], our benchmark shows an increasing trend of incorrect predictions shifting to correct ones as model size increases. This shift results in the significant performance improvements observed in models like Llama3-70B. To understand the disproportionate improvement observed in the 70B model, we analyzed performance breakdown at the task level. These results are visualized and discussed in Figure 9 of Appendix E. In short, we believe the lack of consistency does not necessarily indicate a more robust or advanced reasoning ability, despite the increase of the evaluation results. 6.3 Solution Correctness Prior Table 3: Comparison of average accuracy in identifying the first error step and the corresponding error reason, with and without prior knowledge of the solutions’ correctness. | Gemma-2B Llama3-8B Llama3-70B | 0.3 15.5 14.5 | 0.1 26.4 34.6 | 0.1 6.6 9.1 | 0.0 11.9 25.7 | | --- | --- | --- | --- | --- | | GPT-4-Turbo | 40.9 | 41.6 | 37.9 | 38.0 | Table 4: Comparison of prompting methods: MR-Scores achieved by zero-shot step-wise CoT and Self-Refine technique. | Gemma-2B Llama3-8B Llama3-70B | 0.1 11.7 17.7 | 0.2 11.3 27.5 | | --- | --- | --- | | GPT-4-Turbo | 43.2 | 45.5 | To verify the influence of external ground truth signals, we sampled 100 incorrect solutions from each subject respectively as our test set. By observing the same set of language models under a zero-shot CoT setting, we aim to determine whether the knowledge of the solution’s incorrectness enhances their ability to identify the first error step and the reason for the error. The results in Table 4 illustrate that the benefits of knowing the solution correctness prior generally increase with the model’s competence but begin to plateau at the level of sophisticated models like GPT-4. Specifically, the Gemma-2b model struggles significantly in our benchmark, showing nearly zero performance due to its limited ability to follow formats and comprehend complex tasks. Consequently, having the solution correctness prior does not improve its performance metrics. In contrast, models with moderate capabilities benefit substantially from this prior knowledge, which aids in accurately locating the first error step and elucidating the error reason. However, as model capabilities improve, the incremental benefits of this prior knowledge quickly diminish. For instance, GPT-4 shows only a marginal improvement in identifying the first error step and an almost negligible impact on error reason analysis when provided with the prior. 7 Conclusion This paper highlights the importance of evaluating the reasoning capabilities of LLMs with process-oriented design and presents a comprehensive benchmark called MR-Ben that addresses the limitations of existing evaluation methodologies. MR-Ben consists of questions from a diverse range of subjects and incorporates a meta-reasoning paradigm, where LLMs act as teachers to evaluate the reasoning process. Our evaluation of a diverse suite of LLMs on MR-Ben reveals several key limitations and weaknesses. Many models struggle with identifying and correcting errors within reasoning chains, demonstrating difficulty in performing system-2 style thinking—such as scrutinizing assumptions, calculations, and intermediate steps. Furthermore, even state-of-the-art models often fail to maintain consistency across reasoning paradigms, exposing gaps in their generalization abilities. Additionally, our findings emphasize the importance of searching and reflecting on the solution space during inference. Models like the o1 series showcase the potential of scaling test-time computation, where frequent retrospection and iterative search through multiple solution paths significantly enhance reasoning performance. 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Firstly, its applicability may be restricted when it comes to subjects that are inherently holistic or creative in nature, such as humanities or sociology. These subjects often require a comprehensive understanding and modification (e.g. essay writing), which can be challenging to break down into specific, sequential reasoning steps and corrections. Secondly, MR-Ben is currently confined to questions in English. This could potentially limit the scope of reasoning challenges that can be explored, as different languages may present unique cognitive and linguistic hurdles. Lastly, the analysis and correction of errors in the reasoning steps are currently based on solutions generated by three LLMs, namely GPT-3.5, Mistral-Medium, and Claude 2. It’s important to note that different LLMs and different individuals, may exhibit distinct reasoning and error patterns. Therefore, it would be beneficial to broaden the spectrum of solutions analyzed, incorporating a more diverse range of LLMs and even human responses. This would not only enhance the robustness of the evaluation framework but also provide a more nuanced understanding of the reasoning processes at play. A.2 Broader Impact Positive Societal Impacts The proposed dataset MR-Ben has the potential to bring about significant positive societal impacts. It can contribute to the development and enhancement of LLMs by providing a comprehensive benchmark suite, which researchers and developers can use to identify and address the limitations and weaknesses of their models. This can lead to more accurate, efficient, and reliable LLMs. The meta-reasoning paradigm might open new avenues in AI research, leading to a deeper understanding of reasoning capabilities and the development of innovative methodologies for their evaluation and improvement. Moreover, with a wide range of subjects, MR-Ben can be a valuable resource for educational AI tools, providing personalized learning experiences and helping students understand and improve their reasoning skills. AI systems with improved reasoning capabilities can also be instrumental in various sectors, including healthcare, finance, and environmental management, aiding in complex decision-making and problem-solving tasks. Negative Societal Impacts MR-Ben may also present potential negative societal impacts. As with any technology, there is a risk of LLMs being misused or used maliciously. For instance, LLMs with advanced reasoning capabilities could be used to manipulate information or deceive people. The use of LLMs in decision-making and problem-solving tasks could lead to an over-reliance on these systems, potentially undermining human judgment and critical thinking skills. Advanced LLMs, especially those used in sensitive sectors like healthcare and finance, need to handle vast amounts of data, which can raise privacy and security concerns if not managed properly. A.3 Additional Related Work Improving Reasoning Abilities of LLMs To enhance the reasoning capabilities of LLMs, prior research primarily focuses on specific prompting techniques [11]. Existing efforts include few-shot prompting with intermediate steps augmented demonstrations [66, 72, 69] or zero-shot prompting with specific instructions [36, 74]. Although these methods have shown promising results, their effectiveness is often constrained by their task-specific nature and the labour-intensive process of designing prompts, leading to inconsistent outcomes across different tasks [75, 80]. Another strategy to facilitate reasoning involves instruction tuning or knowledge distillation, which elicits reasoning paths from LLMs without explicit prompting [13, 49, 26, 44]. These approaches typically involve resource-intensive fine-tuning over LLMs and require a large set of examples annotated with CoT. Learning From Feedback Improving LLMs through learning from feedback has become a prevalent strategy, notably through reinforcement learning from human feedback, which seeks to align LLMs with human values by refining their outputs based on feedback [53, 8]. However, this method faces challenges such as high costs due to manual labor and a lack of real-time feedback capabilities [20]. An alternative strategy involves using self-correcting LLMs, which rely on automated feedback to iteratively adapt and understand the consequences of their actions without relying on humans. This feedback can be derived from outside sources such as other models [70, 45], tools [24, 29], knowledge bases [21, 76], evaluation metrics [34, 67] or generation logits [73]. Appendix B Robustness of MR-Score | gpt-4-turbo deepseek_coder Qwen2-72B | 83/55 100/38 99/39 | 137/15 145/7 142/10 | 164/11 167/8 167/8 | 305/46 321/30 312/39 | 194/25 200/19 195/24 | 166/27 172/21 172/21 | 192/16 193/15 200/8 | | --- | --- | --- | --- | --- | --- | --- | --- | Table 5: Scoring of error reasons from different models across subjects. | Agreement Ratio | Coding 7/8 | Phy. 12/13 | Bio. 21/21 | Med. 12/12 | Chem. 15/17 | Logic 15/16 | Math 10/13 | | --- | --- | --- | --- | --- | --- | --- | --- | Table 6: Agreement ratio between the author and the proxy scoring model across different subjects. Question: Does the ACC_reason metric’s dependency on the judgments of different LLMs or human evaluators lead to variability in scoring ? Answer: We would like to argue that due to the careful design of our evaluation mechanism, the automatic scoring of error reasons is both robust and economically feasible: - Multiple annotators: During the annotation stage, we collected multiple annotations for the first error reasons and potential error rectification from different annotators who agreed on the solution correctness and the first error step. - Proxy Model Evaluation: Based on the ground truth annotations collected from various perspectives, the proxy language model (e.g., GPT-4-Turbo) then examines the error reasons provided by evaluating models. Given the question/solution pair and information regarding the first error step, error reasons, and rectification, the potential flaws of the error reasons provided by the evaluating models are easy to diagnose under contrast. - ACC_reason robustness: Table- 5 shows the scores of error reasons sampled from our evaluation results. For the same set of error reasons collected in each subject, three different models made their predictions on correctness/incorrectness. We can clearly see the consistency of their predictions among the three models over questions in all subjects. Since the MR-Score is a weighted metric, the final score variability is less than 1 percent in total. Human-Model Agreement Rate: As mentioned in 3, the agreement rate between manual annotations and the GPT-4 predictions over 100 samples randomly collected from all subjects is 92%. Below is the exact detail of our setup: We randomly collected 100 data instances where the evaluating model correctly identified the solution correctness and the first error step across all subjects. We then manually examined whether the proxy scoring model (e.g., GPT-4-Turbo-2024-04-09) correctly scored the error reasons of the evaluating models. Table- 6 is the detailed composition of the ratio in which the author agrees with the proxy scoring model. The annotation time varies significantly across subjects, as some problems—such as coding and chemistry—can take more than 10 minutes to evaluate, while subjects like biology are easier to assess. This high agreement rate further supports the reliability of our evaluation, thus avoiding the need for manual annotation of potentially 138,000 problems (6,000 benchmark size times 23 models evaluated). Table 7: Evaluation results breakdown on MR-Ben: This table presents a detailed breakdown of each model’s performance evaluated under metric MCC/ACC-step/ACC-reason across different subjects. Here k stands for number of shot and every model we used in this experiment are instruction-tuned. | Model | Bio. | | Phy. | | Math | | Chem. | | Med. | | Logic | | Coding | | Avg. | | | | | | | | | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | $k$ =0 | $k$ =1 | | | MR-Scores | | | | | | | | | | | | | | | | | | | | | | | | | Claude3-Haiku | 5.7 | 5.8 | | 3.3 | 3.5 | | 3.1 | 3.1 | | 6.5 | 6.4 | | 2.0 | 2.0 | | 1.2 | 1.2 | | 9.0 | 0.0 | | 4.4 | 3.1 | | GPT-3.5-Turbo | 3.6 | 6.6 | | 5.7 | 6.7 | | 5.7 | 5.4 | | 4.9 | 6.7 | | 3.6 | 4.4 | | 1.7 | 4.5 | | 3.0 | 4.1 | | 4.0 | 5.5 | | Phi3-3.8B | 13.4 | 12.5 | | 12.7 | 10.8 | | 13.3 | 13.1 | | 16.4 | 17.1 | | 10.2 | 8.1 | | 8.4 | 5.3 | | 9.1 | 10.2 | | 11.9 | 11.0 | | Deepseek-Coder-33B | 7.4 | 5.5 | | 7.8 | 5.6 | | 7.2 | 8.6 | | 7.8 | 7.4 | | 6.0 | 5.5 | | 4.6 | 6.7 | | 8.4 | 4.9 | | 7.0 | 6.3 | | DeepSeek-Coder-7B | 10.5 | 9.9 | | 11.8 | 9.6 | | 11.8 | 12.1 | | 12.3 | 11.9 | | 10.4 | 11.0 | | 9.8 | 10.7 | | 5.0 | 5.8 | | 10.2 | 10.2 | | LLaMA3-8B | 12.0 | 11.9 | | 10.9 | 7.5 | | 15.0 | 9.0 | | 12.6 | 12.7 | | 9.3 | 8.0 | | 9.4 | 9.6 | | 15.8 | 10.0 | | 12.2 | 9.8 | | Qwen1.5-72B | 15.3 | 19.2 | | 12.9 | 13.6 | | 12.0 | 10.0 | | 13.9 | 16.3 | | 11.7 | 14.7 | | 10.4 | 12.9 | | 3.9 | 5.9 | | 11.5 | 13.3 | | DeepSeek-67B | 17.1 | 19.7 | | 14.9 | 17.3 | | 15.4 | 16.2 | | 16.3 | 20.6 | | 14.7 | 12.2 | | 13.6 | 14.3 | | 14.5 | 15.2 | | 15.2 | 16.5 | | LLaMA3-70B | 20.4 | 27.1 | | 17.4 | 20.5 | | 14.9 | 15.8 | | 19.5 | 25.1 | | 16.3 | 19.3 | | 16.3 | 16.8 | | 29.8 | 16.7 | | 19.2 | 20.2 | | Mistral-Large | 22.2 | 28.0 | | 26.7 | 25.4 | | 24.3 | 28.2 | | 24.0 | 27.0 | | 15.9 | 19.3 | | 14.7 | 17.1 | | 21.1 | 21.4 | | 21.3 | 23.8 | | DeepSeek-V2-236B | 30.0 | 37.1 | | 32.2 | 36.5 | | 32.2 | 30.0 | | 32.5 | 35.4 | | 26.5 | 32.4 | | 23.6 | 27.4 | | 34.2 | 27.1 | | 30.2 | 32.3 | | GPT-4-Turbo | 44.7 | 47.3 | | 42.8 | 45.2 | | 44.3 | 45.4 | | 44.0 | 46.0 | | 38.8 | 38.4 | | 34.1 | 33.6 | | 53.6 | 57.3 | | 43.2 | 44.7 | | MCC-Matthews Correlation Coefficient | | | | | | | | | | | | | | | | | | | | | | | | | Claude3-Haiku | 13.96 | 17.72 | | 16.47 | 13.62 | | 15.09 | 10.74 | | 16.54 | 19.96 | | 8.52 | 8.35 | | 6.21 | 4.94 | | 4.36 | 0 | | 11.59 | 10.76 | | GPT-3.5-Turbo | 10.72 | 19.44 | | 16.66 | 21.33 | | 17.48 | 17.45 | | 18.24 | 12.6 | | 11.19 | 13.28 | | 4.07 | 0 | | 12.35 | 12.35 | | 12.96 | 13.78 | | Deepseek-Coder-33B | 7.51 | 8.57 | | 11.73 | 6.81 | | 9.69 | 21.06 | | 9.98 | 7.94 | | 1.62 | 6.28 | | 0 | 0 | | 26.18 | 15.44 | | 9.53 | 9.44 | | Deepseek-Coder-7B | 4.96 | 9.79 | | 8.77 | 6.72 | | 9.05 | 10.82 | | 10.49 | 9.39 | | 5.02 | 3.17 | | 3.22 | 2.58 | | 10.91 | 6.27 | | 7.49 | 6.96 | | LlaMA3-8B | 19.37 | 21.15 | | 16.24 | 18.64 | | 26.55 | 21.87 | | 25.99 | 28.6 | | 14.92 | 18.95 | | 11.8 | 16.24 | | 14.54 | 15.72 | | 18.49 | 20.17 | | Phi3-3.8B | 27.66 | 28.48 | | 21.61 | 21.44 | | 22.29 | 25.17 | | 30.92 | 33.37 | | 17.36 | 14.9 | | 13.03 | 9.56 | | 14.48 | 18.76 | | 21.05 | 21.67 | | Qwen1.5-72B | 33.64 | 42.44 | | 31.4 | 31.56 | | 29.2 | 23.28 | | 35.47 | 36.47 | | 21.76 | 29.64 | | 24.42 | 27.74 | | 13.8 | 15.69 | | 27.1 | 29.55 | | Deepseek-67B | 43.61 | 41.73 | | 24.16 | 28.77 | | 24.95 | 23.87 | | 36.58 | 37.29 | | 27.8 | 28.93 | | 26.74 | 25.09 | | 28.23 | 29.06 | | 30.3 | 30.68 | | LlaMA3-70B | 45.67 | 56.14 | | 40.34 | 41.3 | | 32.76 | 30.94 | | 41.72 | 52.12 | | 33.18 | 37.75 | | 32.0 | 33.87 | | 47.86 | 29.67 | | 39.08 | 40.26 | | Mistral-Large | 41.67 | 49.0 | | 34.24 | 33.47 | | 29.0 | 37.05 | | 41.99 | 47.07 | | 23.76 | 32.05 | | 25.66 | 33.25 | | 37.05 | 33.52 | | 33.34 | 37.92 | | Deepseek-v2-236B | 52.96 | 53.38 | | 41.81 | 46.48 | | 43.75 | 40.53 | | 54.32 | 50.15 | | 37.61 | 44.53 | | 36.36 | 35.41 | | 45.89 | 35.7 | | 44.67 | 43.74 | | GPT-4-Turbo | 63.33 | 62.59 | | 52.9 | 52.7 | | 50.67 | 52.84 | | 53.05 | 54.59 | | 56.79 | 54.66 | | 40.95 | 42.94 | | 52.5 | 57.53 | | 52.88 | 53.98 | | Accuracy of First Error Step | | | | | | | | | | | | | | | | | | | | | | | | | Claude3-Haiku | 2.15 | 3.1 | | 1.4 | 1.12 | | 2.38 | 1.59 | | 1.77 | 4.42 | | 1.69 | 0.68 | | 1.01 | 0.29 | | 0.0 | 0.0 | | 1.49 | 1.6 | | GPT-3.5-Turbo | 2.86 | 4.53 | | 4.2 | 4.76 | | 4.37 | 3.84 | | 2.87 | 8.17 | | 2.37 | 3.05 | | 1.73 | 7.63 | | 0.61 | 2.44 | | 2.72 | 4.92 | | Deepseek-Coder-33B | 14.83 | 10.29 | | 14.94 | 12.36 | | 14.69 | 10.92 | | 15.67 | 16.31 | | 14.54 | 12.43 | | 12.22 | 18.18 | | 5.49 | 3.05 | | 13.2 | 11.93 | | Deepseek-Coder-7B | 21.77 | 18.18 | | 23.28 | 19.83 | | 23.41 | 20.03 | | 24.46 | 23.18 | | 23.29 | 26.09 | | 20.03 | 24.72 | | 4.27 | 6.1 | | 20.07 | 19.73 | | LlaMA3-8B | 14.35 | 14.35 | | 17.53 | 8.62 | | 20.29 | 7.8 | | 14.16 | 11.59 | | 13.13 | 8.41 | | 13.64 | 11.36 | | 17.68 | 9.76 | | 15.83 | 10.27 | | Phi3-3.8B | 12.68 | 11.48 | | 16.38 | 12.07 | | 17.69 | 16.12 | | 18.03 | 17.17 | | 12.78 | 8.76 | | 10.23 | 6.96 | | 8.54 | 9.15 | | 13.76 | 11.67 | | Qwen1.5-72B | 11.48 | 15.31 | | 10.63 | 11.49 | | 10.79 | 9.88 | | 12.45 | 14.38 | | 11.03 | 13.49 | | 8.1 | 10.94 | | 1.83 | 4.27 | | 9.47 | 11.39 | | Deepseek-67B | 13.16 | 19.14 | | 19.25 | 21.84 | | 20.81 | 22.11 | | 17.17 | 23.39 | | 14.71 | 12.08 | | 12.78 | 13.49 | | 12.2 | 14.02 | | 15.72 | 18.01 | | LlaMA3-70B | 15.79 | 22.25 | | 14.66 | 18.39 | | 13.65 | 15.08 | | 17.81 | 22.32 | | 14.36 | 16.64 | | 14.91 | 14.91 | | 26.83 | 13.41 | | 16.86 | 17.57 | | Mistral-Large | 18.38 | 25.54 | | 26.33 | 28.29 | | 27.28 | 33.25 | | 26.05 | 27.59 | | 16.92 | 19.29 | | 14.53 | 14.96 | | 19.51 | 19.51 | | 21.29 | 24.06 | | Deepseek-v2-236B | 27.51 | 35.41 | | 37.64 | 40.23 | | 36.28 | 34.33 | | 33.91 | 37.55 | | 27.5 | 32.57 | | 22.87 | 27.56 | | 33.54 | 26.83 | | 31.32 | 33.5 | | GPT-4-Turbo | 41.77 | 46.06 | | 42.58 | 46.5 | | 46.49 | 47.81 | | 42.6 | 46.8 | | 37.06 | 36.72 | | 29.93 | 33.24 | | 50.61 | 59.15 | | 41.58 | 45.18 | | Accuracy of First Error Reason | | | | | | | | | | | | | | | | | | | | | | | | | Claude3-Haiku | 1.67 | 2.63 | | 0.56 | 0.84 | | 1.19 | 0.93 | | 0.22 | 2.21 | | 1.18 | 0.34 | | 0.72 | 0.29 | | 0.0 | 0.0 | | 0.79 | 1.03 | | GPT-3.5-Turbo | 1.19 | 2.63 | | 2.24 | 1.96 | | 1.85 | 1.46 | | 0.88 | 3.53 | | 1.35 | 1.69 | | 0.72 | 4.17 | | 0.61 | 1.83 | | 1.26 | 2.47 | | Deepseek-Coder-33B | 2.87 | 1.44 | | 2.01 | 1.15 | | 1.69 | 2.21 | | 2.15 | 1.93 | | 2.63 | 1.05 | | 1.85 | 2.56 | | 3.05 | 1.83 | | 2.32 | 1.74 | | Deepseek-Coder-7B | 5.98 | 5.02 | | 6.03 | 4.6 | | 5.98 | 7.93 | | 5.79 | 6.22 | | 4.9 | 5.08 | | 6.25 | 5.54 | | 3.05 | 5.49 | | 5.43 | 5.7 | | LlaMA3-8B | 7.66 | 6.7 | | 4.89 | 2.3 | | 7.28 | 4.55 | | 6.22 | 7.08 | | 4.73 | 3.33 | | 5.97 | 5.97 | | 15.24 | 7.93 | | 7.43 | 5.41 | | Phi3-3.8B | 8.13 | 6.7 | | 6.9 | 5.75 | | 7.02 | 6.37 | | 9.66 | 10.52 | | 5.78 | 5.08 | | 5.4 | 2.7 | | 7.32 | 7.32 | | 7.17 | 6.35 | | Qwen1.5-72B | 10.29 | 12.2 | | 6.9 | 7.76 | | 5.85 | 4.81 | | 6.22 | 9.44 | | 8.06 | 9.46 | | 6.11 | 8.24 | | 1.22 | 3.05 | | 6.38 | 7.85 | | Deepseek-67B | 8.85 | 11.24 | | 8.62 | 10.06 | | 8.32 | 9.49 | | 7.73 | 12.23 | | 9.46 | 5.6 | | 8.81 | 10.37 | | 10.37 | 10.37 | | 8.88 | 9.91 | | LlaMA3-70B | 13.16 | 18.42 | | 9.77 | 13.51 | | 8.58 | 10.27 | | 11.59 | 15.88 | | 10.68 | 13.49 | | 10.94 | 11.08 | | 24.39 | 13.41 | | 12.73 | 13.72 | | Mistral-Large | 15.27 | 21.0 | | 19.05 | 20.45 | | 15.1 | 21.72 | | 16.56 | 18.54 | | 13.03 | 14.21 | | 11.22 | 11.94 | | 17.07 | 17.68 | | 15.33 | 17.94 | | Deepseek-v2-236B | 22.25 | 31.58 | | 25.57 | 30.17 | | 25.1 | 23.28 | | 22.96 | 28.11 | | 21.54 | 27.5 | | 18.89 | 24.15 | | 29.88 | 23.78 | | 23.74 | 26.94 | | GPT-4-Turbo | 39.14 | 42.0 | | 38.38 | 41.46 | | 40.4 | 41.06 | | 36.64 | 42.16 | | 32.83 | 32.83 | | 27.63 | 30.07 | | 50.61 | 56.1 | | 37.95 | 40.81 | Question: Is the MR-Score sensitive to different weightings? Is MR-Score a robust unified metric? Table- 7 shows breakdown performance for models in all four metrics (MR-Score, MCC, ACC_step, and ACC_reason): 1. Metric Robustness: Due to the progressive nature of the definitions of our subtasks (e.g., the success of subsequent tasks depends on the previous ones), we can see the diminishing trend in the scores of MCC, ACC_step, and ACC_reason. However, thanks to the design of our evaluation mechanism and metrics, the score rankings of different models stay in relatively stable order across metrics. In other words, we have not observed any model that excels in determining the solution correctness (thus high in MCC) but is unable to explain the rationale behind it (e.g., low in ACC_reason). 1. Task Difficulties: As shown in the breakdown table, the ACC_reason metric is more discriminative than the MCC metric for competent models but vice versa for the less competent ones. This aligns with our intuition that generally more difficult questions are more discriminative for strong candidates, while weaker ones are simply incapable of solving them. This phenomenon could in part explain why in general the MR-Score is not very sensitive to minor changes in the weightings assigned to the subtasks, since the differentiability of the subtask metrics tends to reconcile with each other under different scenarios. 1. Differentiability and Interpretability: The weights of the MR-Score are ultimately decided by considering both the discriminative ability and the interpretability. To best differentiate models with different evaluation results, we conducted a thorough grid search to investigate the impact of the weightings. Since the weightings calculated returned a few optimal instances, we deliberately selected the one that assigns higher scores to more difficult tasks. We believe the current weighting ratio strikes a good balance between interpretability and differentiation: For example, GPT-4-Turbo, Deepseek-v2-236B, and Mistral-Large achieve 86.4%, 78.5%, and 81.2% respectively in MMLU but score 43.2%, 29.4%, and 21.3% in our benchmark. Appendix C More Discussion on Biases To quantitatively assess the relationship between the length of solutions and their correctness, Pearson-Correlation-Coefficients were calculated and reported in Table- 8 in the Appendix. The result suggests varying dynamics across disciplines regarding how solution length impacts the likelihood of correctness. For subjects such as coding, chemistry and math, longer solutions are less likely to be correct, which could suggest that complexity or elaboration in responses may lead to mistakes or incorrect reasoning. For medicine, despite being weak, there’s a tendency for longer solutions to be slightly more correct, possibly due to more detailed or thorough explanations being favorable. For the other subjects, length of solution does not appear to significantly affect correctness, indicating that other factors likely play a more dominant role in determining solution quality. The overall Pearson Coefficients analysis reflects the distinct nature of problem-solving in each field of our benchmark. Table 8: Pearson Correlation Between Solution Length and Correctness | Medicine Physics Biology | 0.094 -0.061 0.009 | 0.0072 0.111 0.783 | | --- | --- | --- | | Chemistry | -0.127 | 0.00018 | | Coding | -0.199 | 0.0021 | | Logic | 0.0002 | 0.995 | | Math | -0.115 | 0.00049 | Table 9: Evaluation Results of Models on MR-Bean in few-shot settings: This table presents a detailed breakdown of each model’s performance evaluated under metric MR-Score across different subjects. | Gemma-2B 1 2 | 0 0.0 0.1 | 0.1 0.0 0.2 | 0.0 1.0 0.7 | 0.0 0.0 0.6 | 0.1 0.4 0.7 | 0.0 0.2 0.2 | 0.0 0.0 0.0 | 0.7 0.2 0.4 | 0.1 | | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | | 3 | 0.1 | 0.3 | 1.1 | 0.1 | 0.7 | 0.3 | 0.0 | 0.4 | | | Llama3-8B | 0 | 11.1 | 14.9 | 14.8 | 12.8 | 9.4 | 9.6 | 9.1 | 11.7 | | 1 | 11.7 | 8.1 | 7.8 | 12.8 | 7.3 | 10.7 | 5.7 | 9.2 | | | 2 | 9.7 | 7.8 | 11.1 | 8.8 | 6.4 | 6.2 | 2.4 | 7.5 | | | 3 | 10.0 | 10.7 | 8.3 | 8.2 | 5.5 | 5.3 | 3.0 | 7.3 | | | Llama3-70B | 0 | 19.9 | 15.4 | 15.0 | 17.6 | 14.6 | 13.5 | 28.2 | 17.7 | | 1 | 30.5 | 21.4 | 16.8 | 26.2 | 16.9 | 16.0 | 15.3 | 20.4 | | | 2 | 27.2 | 19.9 | 16.8 | 22.0 | 15.9 | 17.5 | 19.5 | 19.8 | | | 3 | 27.2 | 20.6 | 16.3 | 21.1 | 16.0 | 14.6 | 19.4 | 19.3 | | | GPT-4-Turbo | 0 | 44.7 | 42.8 | 44.3 | 44.0 | 38.8 | 34.1 | 53.6 | 43.2 | | 1 | 47.3 | 45.2 | 45.4 | 46.0 | 38.4 | 33.6 | 57.3 | 44.7 | | | 2 | 46.6 | 42.7 | 44.9 | 43.3 | 42.1 | 35.9 | 53.0 | 44.1 | | | 3 | 44.0 | 44.8 | 46.5 | 44.4 | 41.2 | 33.7 | 56.6 | 44.5 | | <details> <summary>extracted/6085487/figures/Mr-Ben-Pipeline.jpg Details</summary>  ### Visual Description ## Diagram: AI Tutoring System ### Overview The image illustrates a conceptual diagram of an AI tutoring system. It shows a pie chart representing different subjects, which are then processed by AI models, resulting in feedback on the correctness of solutions. ### Components/Axes * **Pie Chart:** Divided into seven segments, each representing a subject: Medicine, Chemistry, Biology, Coding, Math, Logic, and Physics. * **AI Processing:** Three AI icons are shown: a green icon resembling the ChatGPT logo, a brown "AI" icon, and a red/orange icon resembling the Wolfram Alpha logo. * **Problem/Solution Cards:** Represented as stacks of cards for Logic, Math, and Coding. Each card contains a "Q:" (Question) and "A:" (Answer) section, followed by multiple "Step" lines. * **Feedback:** Each subject's solution is followed by an icon of people discussing, and a feedback card indicating "Correctness," "First Error Step," "Error Reason," and "Rectified Step." ### Detailed Analysis * **Pie Chart Subjects:** * Medicine (purple) * Chemistry (pink) * Biology (orange) * Coding (yellow) * Math (light green) * Logic (light green) * Physics (light blue) * **AI Processing Icons:** * ChatGPT-like icon (green) * "AI" icon (brown) * Wolfram Alpha-like icon (red/orange) * **Problem/Solution Card Details:** * Each card has a "Q:" and "A:" section, followed by "Step 1:", "Step 2:", and "Step n:". * The content of the "Q:" and "A:" sections is obscured, represented by gray lines. * The "Step" lines are followed by ellipses. * **Feedback Card Details:** * Logic: "Correctness: X", "First Error Step: 2", "Error Reason: ...", "Rectified Step: ..." * Math: "Correctness: ✓", "First Error Step: N/A", "Error Reason: N/A", "Rectified Step: N/A" * Coding: "Correctness: X", "First Error Step: 5", "Error Reason: ...", "Rectified Step: ..." ### Key Observations * The diagram illustrates a flow from subject selection to AI processing to feedback on solutions. * The feedback indicates that the Math problem was solved correctly, while Logic and Coding problems had errors. * The "First Error Step" is specified for incorrect solutions, suggesting a step-by-step analysis. ### Interpretation The diagram represents a conceptual AI tutoring system that covers multiple subjects. The system takes a problem, processes it through AI models, and provides feedback on the correctness of the solution, including the first error step and potential rectification. The use of different AI icons suggests that the system may leverage multiple AI models for different tasks or subjects. The diagram highlights the potential of AI to provide personalized and detailed feedback in education. The "N/A" values for the Math problem suggest that the system can also identify correct solutions. </details> Figure 6: This is the illustration of the dataset creation pipeline of MR-Ben . We first compile a set of questions from different subjects and then collect solutions from different LLMs. For each subject, a group of domain experts is recruited to annotate each question solution pair on its solution correctness, first error step, and error reasons. <details> <summary>x6.png Details</summary>  ### Visual Description ## Prompt: Solution Generation During Dataset Compilation ### Overview The image presents a prompt designed for generating step-by-step solutions during dataset compilation. It outlines the desired format for analyzing a question and its multiple-choice answers, emphasizing a sequential analysis of each choice followed by a joint decision on the final solution. ### Components/Axes * **Title:** Prompt for Solution Generation During Dataset Compilation * **Prompt Instructions:** * Generate a step-by-step analysis for a given question in a specified subject. * Question: {df.iloc[idx]['Question']} * Choice_A: {df.iloc[idx]['Choice_A']} * Choice_B: {df.iloc[idx]['Choice_B']} * Choice_C: {df.iloc[idx]['Choice_C']} * Choice_D: {df.iloc[idx]['Choice_D']} * Analyze each candidate choice sequentially. * Decide which option is the solution in the final step. * Ensure every newline character follows a step indicator. * Step 1: [The first reasoning of the step by step analysis on the candidate choices here] * Step 2: [The second reasoning of the step by step analysis on the candidate choices here] * ... * Step n: [Conclude your analysis and decide which choice to make here] * Solution: Choice_A/B/C/D * Follow the format without any additional introductory or concluding statements. ### Detailed Analysis or ### Content Details The prompt provides a template for generating solutions. It uses placeholders like `{subject}`, `{df.iloc[idx]['Question']}`, `{df.iloc[idx]['Choice_A']}`, etc., indicating that these values will be dynamically populated from a dataframe (`df`) using the index `idx`. The core of the prompt is the step-by-step analysis, where each step provides reasoning for evaluating the candidate choices. The final step requires a conclusion and a decision on the correct choice (A, B, C, or D). ### Key Observations * The prompt is designed for automated solution generation, likely within a data processing pipeline. * The use of placeholders suggests integration with a data source (dataframe). * The emphasis on step-by-step reasoning aims to provide explainability for the generated solutions. * The prompt enforces a specific format to maintain consistency in the generated solutions. ### Interpretation The prompt serves as a blueprint for creating a solution generation system. It aims to automate the process of answering multiple-choice questions by providing a structured approach to analyzing each choice and arriving at a final decision. The step-by-step reasoning component is crucial for understanding the logic behind the generated solutions, making the system more transparent and reliable. The prompt's design suggests a focus on data-driven decision-making, where the answers are derived from information stored in a dataframe. </details> Figure 7: This is the prompt we used for solution generation during the dataset compilation stage. Note that besides coding, every subject question in our dataset takes the form of multiple choice problem. <details> <summary>x7.png Details</summary>  ### Visual Description ## Diagram: Three-Round Interaction Prompt Template for Self-Refine ### Overview The image presents a template for a three-round interaction prompt designed for self-refinement. It outlines a process where a model generates a response, reviews it for errors, and then refines the answer based on the identified problems. The template includes placeholders for the subject of the question/solution pair and the model's responses at each stage. ### Components/Axes The image contains the following components: 1. **Title:** "Three-Round Interaction Prompt Template for Self-Refine" 2. **Initial Prompt:** * "Following is a question/solution pair in subject {sol['Subject']}. Your task is to examine the solutions step by step and determine the solution correctness. If the solution is incorrect, please further find out the first error step and explain the error reason." 3. **Placeholder 1:** "{Generated Response From Evaluated Model}" 4. **Review Prompt:** "Review your previous answer and find problems with your answer" 5. **Placeholder 2:** "{Review Response From Evaluated Model}" 6. **Refinement Prompt:** "Based on the problems you found, improve your answer." 7. **Instruction:** "Please follow the desired response format:" 8. **Placeholder 3:** "{Self-Refined Response From Evaluated Model}" ### Detailed Analysis The template is structured as a sequence of prompts and response placeholders, designed to guide a model through a self-evaluation and refinement process. * **Round 1:** The model receives an initial question/solution pair and generates a response. * **Round 2:** The model reviews its previous answer, identifies problems, and provides a review response. * **Round 3:** The model refines its answer based on the identified problems, generating a self-refined response. The placeholders indicate where the model's responses should be inserted at each stage. ### Key Observations The template emphasizes the importance of error detection and correction in the self-refinement process. It encourages the model to not only generate a solution but also to critically evaluate its own work and improve upon it. ### Interpretation The template aims to improve the quality of model-generated responses by incorporating a self-evaluation loop. By prompting the model to identify and correct its own errors, the template encourages a more iterative and refined approach to problem-solving. The use of placeholders allows for easy integration with different models and subject areas. The template suggests a method for enhancing the accuracy and reliability of AI-generated content through self-critique and iterative refinement. </details> Figure 8: This is the prompt we used for self-refine experiment, note that three consecutive inference calls are made in order to perform the most basic self correction. <details> <summary>x8.png Details</summary>  ### Visual Description ## Pie Chart Grid: Model Performance on Tasks ### Overview The image presents a grid of pie charts comparing the performance of different language models (GPT-4-turbo, Llama-3-70B-Instruct, Llama-3-8B-Instruct, and gemma-2b-it) across three tasks. Each pie chart visualizes the percentage of responses that fall into one of four categories: "Correct to Incorrect", "Incorrect to Correct", "Correct to Correct", and "Incorrect to Incorrect". ### Components/Axes * **Grid Structure:** The pie charts are arranged in a 3x4 grid. Each row represents a task (Task 1, Task 2, Task 3), and each column represents a different language model. * **Pie Chart Segments:** Each pie chart is divided into four segments, each representing a different category of response transition. * **Labels:** Each pie chart has a title indicating the task number and the model name (e.g., "Task 1 - GPT-4-turbo"). * **Percentages:** Each segment of the pie chart is labeled with a percentage value. * **Legend:** Located at the bottom of the image, the legend maps colors to response categories: * Light Blue: "Correct to Incorrect" * Dark Blue: "Incorrect to Correct" * Light Green: "Correct to Correct" * Dark Green: "Incorrect to Incorrect" ### Detailed Analysis **Task 1** * **GPT-4-turbo:** * Correct to Incorrect: 8.5% * Incorrect to Correct: 8.5% * Correct to Correct: 69.7% * Incorrect to Incorrect: 13.4% * **Llama-3-70B-Instruct:** * Correct to Incorrect: 12.1% * Incorrect to Correct: 21.6% * Correct to Correct: 51.9% * Incorrect to Incorrect: 14.4% * **Llama-3-8B-Instruct:** * Correct to Incorrect: 20.8% * Incorrect to Correct: 17.2% * Correct to Correct: 40.9% * Incorrect to Incorrect: 21.1% * **gemma-2b-it:** * Correct to Incorrect: 3.8% * Incorrect to Correct: 6.4% * Correct to Correct: 4.7% * Incorrect to Incorrect: 85.2% **Task 2** * **GPT-4-turbo:** * Correct to Incorrect: 4.8% * Incorrect to Correct: 8.6% * Correct to Correct: 37.0% * Incorrect to Incorrect: 49.5% * **Llama-3-70B-Instruct:** * Correct to Incorrect: 2.1% * Incorrect to Correct: 16.6% * Correct to Correct: 14.9% * Incorrect to Incorrect: 66.4% * **Llama-3-8B-Instruct:** * Correct to Incorrect: 7.0% * Incorrect to Correct: 10.1% * Correct to Correct: 11.1% * Incorrect to Incorrect: 71.9% * **gemma-2b-it:** * Correct to Incorrect: 0.3% * Incorrect to Correct: 0.3% (estimated, value is unreadable) * Correct to Correct: 0.3% (estimated, value is unreadable) * Incorrect to Incorrect: 99.1% **Task 3** * **GPT-4-turbo:** * Correct to Incorrect: 9.8% * Incorrect to Correct: 0.0% * Correct to Correct: 76.6% * Incorrect to Incorrect: 13.6% * **Llama-3-70B-Instruct:** * Correct to Incorrect: 7.2% * Incorrect to Correct: 0.0% * Correct to Correct: 49.9% * Incorrect to Incorrect: 42.9% * **Llama-3-8B-Instruct:** * Correct to Incorrect: 15.3% * Incorrect to Correct: 0.0% * Correct to Correct: 21.6% * Incorrect to Incorrect: 63.1% * **gemma-2b-it:** * Correct to Incorrect: 0.2% * Incorrect to Correct: 0.0% * Correct to Correct: 6.2% (estimated, value is unreadable) * Incorrect to Incorrect: 93.8% ### Key Observations * **GPT-4-turbo** generally has the highest percentage of "Correct to Correct" responses across all three tasks, except for Task 2 where it is outperformed by the other models. * **gemma-2b-it** consistently shows a very high percentage of "Incorrect to Incorrect" responses, indicating it struggles with these tasks. * **Llama-3-70B-Instruct** and **Llama-3-8B-Instruct** show varying performance depending on the task, with a tendency towards higher "Incorrect to Incorrect" percentages compared to GPT-4-turbo. * For Task 3, all models have 0.0% "Incorrect to Correct" responses. ### Interpretation The pie charts provide a visual comparison of the performance of different language models on a set of tasks. The "Correct to Correct" percentage can be interpreted as a measure of the model's accuracy and consistency. The "Incorrect to Incorrect" percentage indicates the model's tendency to consistently fail on certain inputs. The "Correct to Incorrect" and "Incorrect to Correct" percentages may reflect the model's ability to learn or adapt during the task. The data suggests that GPT-4-turbo generally outperforms the other models in terms of accuracy and consistency. gemma-2b-it appears to struggle significantly with these tasks. The Llama models show intermediate performance, with some variability depending on the specific task. The fact that all models have 0% "Incorrect to Correct" responses for Task 3 could indicate that this task is particularly challenging or that the models are not designed to correct their mistakes in this specific context. </details> Figure 9: This is the performance breakdown for self-refine experiment in the task level, where task 1,2,3 refers to solution correctness, first error step and error reason determination. <details> <summary>x9.png Details</summary>  ### Visual Description ## Prompt: Response Generation ### Overview The image is a prompt for generating responses to question/solution pairs. It outlines the task, definitions, and desired response format. ### Components/Axes * **Title:** Prompt for Response Generation * **Instructions:** * Examine solutions step-by-step to determine correctness. * Identify the first error step and explain the reason. * **Definitions:** * Solution Correctness: Does the solution correctly answer the question with justifiable reasoning and selected the corrected options? * First Error Step: Categorizes steps as correct, neutral, or incorrect based on logic, computation, and potential to lead to the correct answer. * Error Reason: Specifies the errors made in the identified first error step and suggests a rectified reasoning step. * **Input Fields:** * Question: {sol['Question']} * Options: {sol['Options']} * Step by Step Solution: {sol['Model\_Solution\_Steps']} * **Response Format:** * Solution Analysis: \[Give a step by step analysis on the solution correctness here] * Solution Correctness: \[Input 'correct'/'incorrect' here to indicate the overall correctness of the solution] * First Error Step: \[Input 'Step x' here to indicate the first error step here. Input 'N/A' if the solution is correct.] * Error Reason: \[Input the error reason and the rectified reasoning of the first error step here. Input 'N/A' if the solution is correct.] ### Detailed Analysis or ### Content Details The prompt provides a structured approach to evaluating solutions. It defines criteria for correctness, error identification, and provides a template for generating responses. The prompt emphasizes the need to identify the first error step and provide a rectified reasoning. ### Key Observations The prompt is designed to standardize the evaluation and response generation process for question/solution pairs. It provides clear definitions and a structured format to ensure consistency. ### Interpretation The prompt aims to facilitate automated or semi-automated evaluation of solutions. By providing clear guidelines and a structured format, it enables consistent and objective assessment of solution correctness and error identification. The prompt is designed to be used in a system where questions, options, and model solutions are provided, and the system generates an analysis, correctness assessment, error identification, and error reason. </details> Figure 10: This is the prompt template we used to evaluate all the models. The k-shot-demo and hint-sent are either the few shot examples and solution correctness prior or empty string, depending on the experiment setup. <details> <summary>x10.png Details</summary>  ### Visual Description ## Prompt: Scoring Error Reasons ### Overview The image presents a prompt designed to evaluate a student's explanation of an error in a problem solution. It outlines the task for an experienced teacher, specifying the need to understand the problem, the incorrect solution, and the ground truth behind the error. The prompt requests a detailed analysis focusing on the alignment between the student's explanation and the actual error. It also provides a structured format for the evaluation, including step-by-step reasoning, student error analysis, and a final decision. ### Components/Axes * **Title:** Prompt for Scoring Error Reasons * **Introduction:** As an experienced {data['Subject']} teacher, your assistance is required to evaluate a student's explanation regarding the error in a problem solution. The task involves a detailed understanding of the problem, the incorrect solution provided, and the ground truth behind the error. Your analysis should focus on whether the student's explanation aligns with the actual error in the solution. * **Details Section:** * Question: {data['Question']} * Incorrect Solution Provided: {data['Model\_Solution\_Steps']} * First Incorrect Step in the Solution: {data['Model\_Solution\_First\_Error\_Step']} * Ground Truth Error Reasons: {data['Model\_Solution\_Error\_Reason']} * Ground Truth Rectified Steps: {data['Model\_Solution\_Rectified\_First\_Error\_Step']} * Student's Explanation of the Error: {data['Evaluation\_Result']['error\_reason']} * **Instructions Section:** * Step-by-Step Reasoning: \[Offer a succinct, step-by-step interpretation of the ground truth error reason.] * Student Error Reason Analysis: \[Analyze the student's explanation step by step, determining its accuracy in reflecting the actual error briefly.] * Final Decision: \[State only 'Correct' or 'Wrong' to conclude whether the student's explanation correctly identifies the error based on your analysis.] * **Concluding Statement:** Please follow this format without any additional introductory or concluding statements. ### Detailed Analysis or ### Content Details The prompt is structured to guide the evaluation of a student's explanation of an error. It begins by setting the context and defining the evaluator's role. It then lists the specific details needed for the evaluation, including the question, the incorrect solution, the first incorrect step, the ground truth error reasons, the ground truth rectified steps, and the student's explanation. Finally, it provides a structured format for the evaluation, requiring step-by-step reasoning, student error analysis, and a final decision. ### Key Observations The prompt uses placeholders (e.g., {data['Subject']}) to indicate where specific information should be inserted. The instructions are clear and concise, providing a structured approach to evaluating the student's explanation. The prompt emphasizes the importance of aligning the student's explanation with the actual error in the solution. ### Interpretation The prompt is designed to ensure a consistent and thorough evaluation of student explanations of errors. By providing a structured format and specific details, it aims to reduce subjectivity and improve the accuracy of the evaluation. The use of placeholders allows for the prompt to be adapted to different subjects and problems. The prompt's focus on aligning the student's explanation with the actual error highlights the importance of understanding the underlying reasons for errors in problem-solving. </details> Figure 11: This is the prompt template we used to request GPT-4 to help us score the error reasons explained by evaluated models. Note that despite the difficulties of deciding the solution correctness, it is much easier to decide the error reason correctness given the ground truth annotations. Appendix D Evaluation Prompt Figure- 10 is the prompt template we used to evaluate all the models in our paper. Note that with minor modifications on the following template, the evaluation results can be heavily affected. For example, by introducing a simple hint sentence "Hint: This solution is incorrect. Please focus on looking for the First Error Step and Error Reason.", the model performance can drastically improve as shown in 4. Also, by simply taking away the line of ’Solution Analysis’ in the response format part of the prompt, the evaluated model will directly output the scoring result without step-wise COT analysis on the solution. This setup will lead to a near zero MR-Score performance as discussed in Section- 5. Figure- 7 is the prompt we used to query language models for solution generation during the dataset compilation phase. Note that in the prompt, we specifically asked the model to analyse each option in the multiple-choice problem. This is crucial in examining if the model possesses a comprehensive understanding on the topics that the question is asking. Figure- 11 shows the prompt we used to query GPT-4 to score the error reasons returned from evaluated models. Despite the challenging nature of the original task to determine the solution correctness, it is a much easier job to determine if the error reason from the evaluated models aligns with the ground truth error reason. Figure- 8 demonstrates the prompt template we used for self-refine experiment. Note that we followed the setting of [31] without introducing any prior assumptions or knowledge. This minimum version of extra prompting would mostly rely on the capability of language models to perform self-refine procedure. Appendix E Self Refine Analysis In this section, we present the results of self-refine in the task level. Specifically, we are looking at the change of labelling by the evaluated models in the determination of solution correctness as shown by Figure- 9. We summarize our observation below: - Small Models like Gemma-2B are too limited to perform effective self-reflection. - Competent Models like GPT4-Turbo are confident in their initial decisions, hardly switching their decisions during self-reflection. - Intermediate Models like Llama3-70B exhibit substantial changes during self-reflection, indicating a lack of consistency in their decisions. However, its change of decisions from incorrect to correct happens to be significantly higher in locating the first error step than in examining solution correctness and explaining the error reason, therefore boosting the overall MR-Score by a large margin. We believe the lack of consistency does not necessarily indicate a more robust or advanced reasoning ability, despite the increase in the evaluation results. - Conclusion: Our results support the observation that LLMs generally lack effective self-refinement capabilities [31]. Appendix F Error Analysis We provide qualitative analyses of how GPT-4 as an example model performed on our benchmark across all seven subjects. The purpose is to offer a deeper understanding of the types and causes of errors made by experimented models to inform future improvements. For each subject in the subsections below, a failure case and a success case are listed. Following the MR-Ben evaluation framework, each case demonstration consists of the following parts: (1) original questions, options, ground-truth final answers, and LLM-generated CoT solutions; (2) human annotations of step-wise error detection, explanation, and correction; (3) evaluation annotation from the experimented GPT-4 on the aforementioned LLM-generated CoT solutions; (4) scoring results of the error reason if the experimented model identifies the correct first error step. From our analysis of sampled failure cases, several general observations are made. Firstly, the assessed model GPT-4 exhibits a widely resistant ‘false positive bias’ on our benchmark across all subjects: In cases where the LLM makes incorrect evaluations, the proportion of type I errors is much higher than type II errors. In other words, GPT-4 tends to overlook the mistakes that exist in incorrect model solutions and mislabel them as correct, while seldom actively mislabeling correct model solution steps as incorrect steps. In fact, among the 42 sampled cases we surveyed spanning the seven subjects, all failure cases (size = 21) belong to the type I error category. We provide two possible explanations for such bias: (a) input bias: the implemented LLMs are instructed-tuned, and are inherently biased to follow the prompt input. Therefore, even when the models are asked to fairly judge these CoT solution steps in the prompt input in binary labels, it is likely their labeling threshold is affected and biased towards positive judgments. This is a common issue in using LLMs as generation evaluators and may be mitigated by adjusting the prompt design or other debiasing methods [42, 79]; (b) self-preference bias: it has drawn recent attention that state-of-the-art models display self-preference bias: the phenomenon in which an LLM inherently favors their own generated output over texts from other LLMs and humans [43, 54]. Therefore, the experiment results of LLMs that are under the same family of the three sampled models (GPT-3.5-Turbo-0125 [50], Claude2 [5], and Mistral-Medium [32]) may be affected. With the increasingly extensive use of self-evaluation and LLM-as-judge methods, we call for future researchers’ attention to the potential issue. Secondly, the MR-Ben benchmark revealed many intricate cases where the assessed model GPT-4 reached a correct final answer through incorrect solution steps, challenging the models’ multi-step reasoning capabilities to a greater scale. As shown in the failure cases in math, physics, biology, etc., our benchmark evaluation is able to identify step errors that the sample model made in the solution steps even when its final answer matches the final ground-truth choice. While such step errors can be trivial in terms of generating the correct final answer in the demonstrated failure cases, they can become significant in just slightly nuanced questions, as mentioned in the error analysis section of MMLU [27]. In contrast, our framework, by decomposing the question and model solutions, remains relatively immune to the nuances in question framing. This highlights an important significance of our MR-Ben benchmark in that it is not only elaborate but also robust compared to previous benchmarks. Lastly, there are subtle nuances of model performance in different reasoning paradigms manifest in the case demonstrations of specific subjects. They are interpreted case by case by the captioned figures listed below. See pages - of error_analysis/all.pdf Appendix G Computational Resources Used In this paper, all experiments are either performed on open-source models with local inference or closed-source models with API calls. For local inference, we are using A800 machines with 8 GPUs to run the inferences. The total evaluation time on our 6k benchmark on the 70B language models typically takes around 2 hours using fast inference libraries such as vllm. For smaller language models such as Phi-3 or Gemma, the compute time is smaller. Appendix H Annotation Guidelines Below we provide the original annotation guidelines distributed to annotators of distinctive subjects included in the MR-Ben benchmark: math, biology, physics, chemistry, logic, medicine, and coding. The guidelines serve as the primary training material and instructions for annotators to complete the labeling tasks, specified with detailed descriptions, requirements, and standards. See pages - of annotation_guidelines/math.pdf See pages - of annotation_guidelines/physics.pdf See pages - of annotation_guidelines/biology.pdf See pages - of annotation_guidelines/medicine.pdf See pages - of annotation_guidelines/chemistry.pdf See pages - of annotation_guidelines/logic.pdf See pages - of annotation_guidelines/coding.pdf NeurIPS Paper Checklist 1. Claims 1. Question: Do the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope? 1. Answer: [Yes] 1. Justification: Yes, the main claims made in the abstract and introduction accurately reflect the paper’s contributions and scope. 1. Guidelines: - The answer NA means that the abstract and introduction do not include the claims made in the paper. - The abstract and/or introduction should clearly state the claims made, including the contributions made in the paper and important assumptions and limitations. 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Reviewers will be specifically instructed to not penalize honesty concerning limitations. 1. Theory Assumptions and Proofs 1. Question: For each theoretical result, does the paper provide the full set of assumptions and a complete (and correct) proof? 1. Answer: [N/A] 1. Justification: Our work does not involve theoretical assumptions and proofs. 1. 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Question: Does the paper fully disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and/or conclusions of the paper (regardless of whether the code and data are provided or not)? 1. Answer: [Yes] 1. Justification: We disclose all the information needed to reproduce the main experimental results of the paper to the extent that it affects the main claims and conclusions of the paper, detailed in § 5.1. 1. 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In general. releasing code and data is often one good way to accomplish this, but reproducibility can also be provided via detailed instructions for how to replicate the results, access to a hosted model (e.g., in the case of a large language model), releasing of a model checkpoint, or other means that are appropriate to the research performed. - While NeurIPS does not require releasing code, the conference does require all submissions to provide some reasonable avenue for reproducibility, which may depend on the nature of the contribution. For example 1. If the contribution is primarily a new algorithm, the paper should make it clear how to reproduce that algorithm. 1. If the contribution is primarily a new model architecture, the paper should describe the architecture clearly and fully. 1. 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See the NeurIPS code and data submission guidelines (https://nips.cc/public/guides/CodeSubmissionPolicy) for more details. - The authors should provide instructions on data access and preparation, including how to access the raw data, preprocessed data, intermediate data, and generated data, etc. - The authors should provide scripts to reproduce all experimental results for the new proposed method and baselines. If only a subset of experiments are reproducible, they should state which ones are omitted from the script and why. - At submission time, to preserve anonymity, the authors should release anonymized versions (if applicable). - Providing as much information as possible in supplemental material (appended to the paper) is recommended, but including URLs to data and code is permitted. 1. Experimental Setting/Details 1. Question: Does the paper specify all the training and test details (e.g., data splits, hyperparameters, how they were chosen, type of optimizer, etc.) necessary to understand the results? 1. Answer: [Yes] 1. Justification: We provide comprehensive dataset statistics, evaluation metric descriptions, hyperparameters, and tool usage in § 5. 1. Guidelines: - The answer NA means that the paper does not include experiments. - The experimental setting should be presented in the core of the paper to a level of detail that is necessary to appreciate the results and make sense of them. - The full details can be provided either with the code, in appendix, or as supplemental material. 1. Experiment Statistical Significance 1. Question: Does the paper report error bars suitably and correctly defined or other appropriate information about the statistical significance of the experiments? 1. Answer: [Yes] 1. 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Question: Does the paper describe safeguards that have been put in place for responsible release of data or models that have a high risk for misuse (e.g., pretrained language models, image generators, or scraped datasets)? 1. Answer: [N/A] 1. Justification: Our dataset focuses on evaluation rather than training models. We leverage existing datasets rather than scrape from the Internet. 1. Guidelines: - The answer NA means that the paper poses no such risks. - Released models that have a high risk for misuse or dual-use should be released with necessary safeguards to allow for controlled use of the model, for example by requiring that users adhere to usage guidelines or restrictions to access the model or implementing safety filters. - Datasets that have been scraped from the Internet could pose safety risks. 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