## Table: LM Answer Identification Example ### Overview The image presents an example of how a Language Model (LM), specifically LLaMA2-7B, identifies the better answer to a question. It includes the question, a reference answer, a greedy answer, and two other possible answers. A table provides metrics for each answer, including Rouge-1 score, maximum probability, average probability, maximum entropy, average entropy, Gb-S, Wb-S, Bb-S, SU, and Ask4-conf. ### Components/Axes * **Title:** An example that the LM identifies the better answer (LM: LLaMA2-7B) * **Question:** Which musical featured the songs A Secretary is Not A Toy, and The Company Way? * **Answers:** * Ref answer: How to Succeed in Business Without Really Trying * Greedy answer: The Pajama Game * Answer 1: How to Succeed In Business Without Really Trying * Answer 2: The Company Way * **Table Headers:** * Rouge-1 * Max Prob * Avg Prob * Max Ent * Avg Ent * Gb-S * Wb-S * Bb-S * SU * Ask4-conf * **Table Rows:** * Ref answer * Greedy answer * Answer 1 * Answer 2 ### Detailed Analysis or ### Content Details The table presents the following data: | | Rouge-1 | Max Prob | Avg Prob | Max Ent | Avg Ent | Gb-S | Wb-S | Bb-S | SU | Ask4-conf | | :-------------------- | :------ | :------- | :------- | :------ | :------ | :--- | :--- | :--- | :--- | :-------- | | Ref answer | 1 | 0.12 | 0.96 | 0.43 | 0.93 | 0.23 | 0.33 | | | | | Greedy answer | 0 | 0.12 | 0.9 | 0.37 | 0.82 | 0.09 | 0.14 | 0.33 | 0.08 | 0 | | Answer 1 | 1 | 0.08 | 0.93 | 0.43 | 0.94 | 0.14 | 0.22 | | | | | Answer 2 | 0 | 0.01 | 0.78 | 0.37 | 0.6 | 0.08 | 0.13 | | | | ### Key Observations * The "Ref answer" and "Answer 1" have the highest Rouge-1 scores (1), indicating they are the closest to the reference answer based on the Rouge-1 metric. * The "Ref answer" has the highest average probability (0.96). * The "Greedy answer" has the lowest Ask4-conf score (0). * "Answer 2" has the lowest Max Prob (0.01) and Avg Prob (0.78) ### Interpretation The table provides a quantitative comparison of different answers generated by the LM against a reference answer. The metrics suggest that the LM identifies "Ref answer" and "Answer 1" as better answers, as indicated by their higher Rouge-1 scores and average probabilities. The "Greedy answer" and "Answer 2" perform worse according to these metrics. The data demonstrates how different metrics can be used to evaluate the quality of answers generated by a language model.

## Data Table: LM Answer Evaluation ### Overview The image presents a data table comparing the performance of different Large Language Model (LLM) answers to a specific question. The table evaluates the answers based on several metrics, including Rouge-1, Max Prob, Avg Prob, Max Ent, Avg Ent, Gb-S, Wb-S, Bb-S, SU, and Ask4-conf. The question being answered is "Which musical featured the songs A Secretary Is Not A Toy, and The Company Way?". ### Components/Axes The table has the following structure: * **Rows:** Represent different answers: "Ref answer", "Greedy answer", "Answer 1", and "Answer 2". * **Columns:** Represent evaluation metrics: "Rouge-1", "Max Prob", "Avg Prob", "Max Ent", "Avg Ent", "Gb-S", "Wb-S", "Bb-S", "SU", and "Ask4-conf". * **Header:** The first row contains the column headers, defining the metrics being evaluated. * **Question:** The question being answered is stated above the table. * **Answers:** The correct answer ("Ref answer") and the LLM generated answers are listed. ### Detailed Analysis or Content Details Here's a reconstruction of the data table's content: | | Rouge-1 | Max Prob | Avg Prob | Max Ent | Avg Ent | Gb-S | Wb-S | Bb-S | SU | Ask4-conf | | :-------------- | :------ | :------- | :------- | :------ | :------ | :--- | :--- | :--- | :---- | :-------- | | Ref answer | 1 | 0.12 | 0.96 | 0.43 | 0.93 | 0.23 | 0.33 | | | | | Greedy answer | 0 | 0.12 | 0.9 | 0.37 | 0.82 | 0.09 | 0.14 | 0.33 | 0.08 | 0 | | Answer 1 | 1 | 0.08 | 0.93 | 0.43 | 0.94 | 0.14 | 0.22 | | | | | Answer 2 | 0 | 0.01 | 0.78 | 0.37 | 0.6 | 0.08 | 0.13 | | | | **Answers:** * **Question:** Which musical featured the songs A Secretary Is Not A Toy, and The Company Way? * **Ref answer:** How to Succeed in Business Without Really Trying * **Greedy answer:** The Pajama Game * **Answer 1:** How to Succeed In Business Without Really Trying * **Answer 2:** The Company Way ### Key Observations * The "Ref answer" consistently scores high on Rouge-1 (1) and Avg Prob (0.96). * The "Greedy answer" has a Rouge-1 score of 0, indicating it does not match the reference answer well. * "Answer 1" matches the "Ref answer" and has a Rouge-1 score of 1 and an Avg Prob of 0.93. * "Answer 2" has the lowest scores across most metrics, suggesting it is the least accurate answer. * The "Ask4-conf" metric is 0 for the "Greedy answer", indicating low confidence in that answer. ### Interpretation The data suggests that the LLM's "Greedy answer" and "Answer 2" are poor responses to the given question, while "Answer 1" is a good response. The "Ref answer" serves as the gold standard, and the metrics are used to quantify how closely the LLM-generated answers align with this standard. The Rouge-1 score is a binary indicator of exact match, while the probability-based metrics (Max Prob, Avg Prob) and entropy-based metrics (Max Ent, Avg Ent) provide more nuanced assessments of answer quality. The Gb-S, Wb-S, Bb-S, SU, and Ask4-conf metrics likely represent other specific evaluation criteria, but their exact meanings are not provided in the image. The overall pattern indicates that the LLM struggles to provide accurate answers to this question, with the "Greedy answer" being the least reliable.

\n ## Technical Document Screenshot: Language Model Answer Evaluation Example ### Overview This image is a screenshot from a technical document or research paper. It presents an example of how a Language Model (LM), specifically LLaMA2-7B, evaluates different answers to a factual question. The image consists of two main parts: a textual example of a question and multiple candidate answers, followed by a data table comparing various evaluation metrics for those answers. ### Components/Axes The image is structured into two primary regions: 1. **Header/Example Region (Top):** Contains the title and a question-answer example. 2. **Data Table Region (Bottom):** A table with numerical metrics. **Header/Example Region Details:** * **Title:** "An example that the LM identifies the better answer (LM: LLaMA2-7B)" * **Question:** "Which musical featured the songs A Secretary is Not A Toy, and The Company Way?" * **Reference Answer (Ref answer):** "How to Succeed in Business Without Really Trying" (displayed in blue text). * **Greedy Answer:** "The Pajama Game" (displayed in red text). * **Answer 1:** "How to Succeed In Business Without Really Trying" * **Answer 2:** "The Company Way" * **Icons:** Small robot icons are placed next to "Greedy answer", "Answer 1", and "Answer 2". **Data Table Structure:** The table has 10 columns and 4 data rows. * **Column Headers (Metrics):** 1. Rouge-1 2. Max Prob 3. Avg Prob 4. Max Ent 5. Avg Ent 6. Gb-S 7. Wb-S 8. Bb-S 9. SU 10. Ask4-conf * **Row Labels (Answer Types):** 1. Ref answer 2. Greedy answer 3. Answer 1 4. Answer 2 ### Detailed Analysis / Content Details **Transcription of the Question & Answer Example:** * **Question:** Which musical featured the songs A Secretary is Not A Toy, and The Company Way? * **Ref answer:** How to Succeed in Business Without Really Trying * **Greedy answer:** The Pajama Game * **Answer 1:** How to Succeed In Business Without Really Trying * **Answer 2:** The Company Way **Data Table Reconstruction:** The following table lists the exact numerical values as they appear in the image. Empty cells indicate no data was provided for that metric-answer combination. | Answer Type | Rouge-1 | Max Prob | Avg Prob | Max Ent | Avg Ent | Gb-S | Wb-S | Bb-S | SU | Ask4-conf | |---------------|---------|----------|----------|---------|---------|-------|-------|-------|-------|-----------| | **Ref answer** | 1 | 0.12 | 0.96 | 0.43 | 0.93 | 0.23 | 0.33 | | | | | **Greedy answer** | 0 | 0.12 | 0.9 | 0.37 | 0.82 | 0.09 | 0.14 | 0.33 | 0.08 | 0 | | **Answer 1** | 1 | 0.08 | 0.93 | 0.43 | 0.94 | 0.14 | 0.22 | | | | | **Answer 2** | 0 | 0.01 | 0.78 | 0.37 | 0.6 | 0.08 | 0.13 | | | | ### Key Observations 1. **Answer Correctness:** The "Ref answer" and "Answer 1" are factually correct (the musical is *How to Succeed in Business Without Really Trying*). The "Greedy answer" (*The Pajama Game*) is incorrect. "Answer 2" (*The Company Way*) is a song title from the correct musical, not the musical itself. 2. **Metric Correlation with Correctness:** * **Rouge-1:** Correct answers (Ref, Answer 1) score 1. Incorrect answers (Greedy, Answer 2) score 0. * **Probability Metrics (Max/Avg Prob):** The correct "Ref answer" has the highest Avg Prob (0.96). The incorrect "Greedy answer" has a relatively high Max Prob (0.12, tied with Ref) but lower Avg Prob (0.9). "Answer 2" has the lowest probabilities. * **Entropy Metrics (Max/Avg Ent):** Correct answers have higher entropy values (0.43/0.93-0.94) compared to incorrect ones (0.37/0.6-0.82), suggesting the model is less certain about incorrect outputs. * **Specialized Scores (Gb-S, Wb-S, etc.):** The "Ref answer" scores highest on Gb-S (0.23) and Wb-S (0.33). The "Greedy answer" has unique, non-zero values for Bb-S (0.33), SU (0.08), and Ask4-conf (0), which are absent for other answers. 3. **Data Completeness:** The metrics Bb-S, SU, and Ask4-conf are only reported for the "Greedy answer". ### Interpretation This image serves as a qualitative example to illustrate how a language model's internal metrics can be used to distinguish between better and worse answers, even when the model itself might generate an incorrect answer via greedy decoding. * **What it demonstrates:** The table shows that the "better" answers (Ref and Answer 1) are characterized by high **Rouge-1** (lexical overlap with a ground truth), high **average probability** (the model assigns high likelihood to the sequence), and high **entropy** (indicating the model's confidence is distributed, not peaked on a single wrong token). The incorrect "Greedy answer" fails on Rouge-1 and has lower average probability and entropy. * **Relationship between elements:** The example sets up a clear contrast. The textual part shows the *output* (answers), while the table quantifies the model's *internal state* when generating those outputs. It argues that metrics like Avg Prob and Avg Ent can serve as proxies for answer quality, potentially for reranking or filtering generated text. * **Notable Anomalies/Insights:** The most striking insight is that the model's greedy search (which picks the most likely next token at each step) produced an incorrect answer ("The Pajama Game"), while other sampled answers (Answer 1) were correct. This highlights a known limitation of greedy decoding. Furthermore, the specialized scores (Gb-S, Wb-S, Bb-S, SU, Ask4-conf) appear to be diagnostic tools that provide different signals for different answer types, with the "Greedy answer" triggering unique values in the last three columns. This suggests these metrics might be designed to detect specific failure modes or characteristics of generated text.

## Table: Answer Comparison Metrics for Musical Question ### Overview This table compares the performance of different answers to the question: *"Which musical featured the songs A Secretary is Not A Toy, and The Company Way?"* Metrics include Rouge-1, probability distributions (Max/Avg), entropy (Max/Avg), and other evaluation scores (Gb-S, Wb-S, Bb-S, SU, Ask4-conf). The reference answer is highlighted as the correct response, while the greedy answer and two alternative answers are evaluated against it. ### Components/Axes - **Headers**: - Rouge-1 - Max Prob - Avg Prob - Max Ent - Avg Ent - Gb-S - Wb-S - Bb-S - SU - Ask4-conf - **Rows**: - **Ref answer** (Reference Answer) - **Greedy answer** - **Answer 1** - **Answer 2** - **Annotations**: - Question and reference answer are highlighted in a yellow box. - Greedy answer is marked with a robot icon and labeled "Greedy answer" in red. - Answer 1 and Answer 2 are labeled with robot icons. ### Detailed Analysis | Metric | Ref answer | Greedy answer | Answer 1 | Answer 2 | |--------------|------------|---------------|----------|----------| | Rouge-1 | 1 | 0 | 1 | 0 | | Max Prob | 0.12 | 0.12 | 0.08 | 0.01 | | Avg Prob | 0.96 | 0.9 | 0.93 | 0.78 | | Max Ent | 0.43 | 0.37 | 0.43 | 0.37 | | Avg Ent | 0.93 | 0.82 | 0.94 | 0.6 | | Gb-S | 0.23 | 0.09 | 0.14 | 0.08 | | Wb-S | 0.33 | 0.14 | 0.22 | 0.13 | | Bb-S | - | 0.33 | - | - | | SU | - | 0.08 | - | - | | Ask4-conf | - | 0 | - | - | ### Key Observations 1. **Reference Answer Dominance**: - Rouge-1 = 1 (perfect match) and Avg Prob = 0.96 (highest probability). - Max Prob = 0.12 (tied with greedy answer but outperforms others). 2. **Greedy Answer Limitations**: - Rouge-1 = 0 (no match) but shares Max Prob = 0.12 with the reference answer. - Avg Prob = 0.9 (lower than reference) and Avg Ent = 0.82 (higher entropy, indicating less confidence). 3. **Answer 1 vs. Answer 2**: - Answer 1 matches Rouge-1 = 1 but has lower Max Prob (0.08) and Avg Prob (0.93) compared to the reference. - Answer 2 has the lowest Avg Prob (0.78) and Avg Ent (0.6), indicating poor performance. 4. **Anomalies**: - Bb-S and SU scores are only populated for the greedy answer and Answer 1, suggesting these metrics may not apply to all answers. - Ask4-conf = 0 for the greedy answer, implying no confidence in its correctness. ### Interpretation The table demonstrates that the **reference answer** ("How to Succeed in Business Without Really Trying") is the most accurate and confident response, as evidenced by its perfect Rouge-1 score and highest average probability. The **greedy answer** ("The Pajama Game") fails to match the reference but shares some probability metrics, likely due to partial overlap in keywords. **Answer 1** ("How to Succeed In Business Without Really Trying") is a close variant of the reference but has slightly lower confidence metrics. **Answer 2** ("The Company Way") performs worst across all metrics, confirming it as the least relevant. The data highlights the importance of precise keyword matching (Rouge-1) and probabilistic confidence (Avg Prob) in evaluating answer quality. The greedy answer’s high entropy (Avg Ent = 0.82) suggests it is less certain, while the reference answer’s low entropy (0.93) reflects higher confidence. The absence of Bb-S and SU scores for some answers may indicate limitations in the evaluation framework or incomplete data.