## Chart/Diagram Type: Performance Comparison Table ### Overview The image presents a performance comparison table for a Language Model (LLaMA2-7B) when answering the question: "Who had a 70s No 1 hit with Billy, Don't Be A Hero?". The table compares the reference answer, the greedy answer from the model, and three other possible answers generated by the model. The comparison is 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. ### Components/Axes * **Title:** An example of a confidently wrong answer (LM: LLaMA2-7B) * **Question:** Who had a 70s No 1 hit with Billy, Don't Be A Hero? * **Ref answer:** Bo Donaldson & The Heywoods * **Greedy answer:** Paper Lace * **Answer 1:** Bo Donaldson * **Answer 2:** Paperchaser * **Answer 3:** Paper Moon * **Columns:** * Rouge-1 * Max Prob * Avg Prob * Max Ent * Avg Ent * Gb-S * Wb-S * Bb-S * SU * Ask4-conf * **Rows:** * Ref answer * Greedy answer * Answer 1 * Answer 2 * Answer 3 ### Detailed Analysis or ### Content Details The table contains 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 | 0.13 | 0.94 | 0.82 | 0.94 | 0.21 | 0.31 | | | | | **Greedy answer** | 0 | 0.79 | 0.99 | 0.86 | 0.94 | 0.82 | 0.83 | 0.72 | 0.31 | 0 | | **Answer 1** | 0.67 | 0.13 | 0.9 | 0.82 | 0.9 | 0.1 | 0.25 | | | | | **Answer 2** | 0 | 0 | 0.81 | 0.7 | 0.82 | 0.08 | 0.12 | | | | | **Answer 3** | 0 | 0 | 0.82 | 0.86 | 0.89 | 0.1 | 0.2 | | | | * **Ref answer:** Rouge-1 score is 1, Max Prob is 0.13, Avg Prob is 0.94, Max Ent is 0.82, Avg Ent is 0.94, Gb-S is 0.21, and Wb-S is 0.31. * **Greedy answer:** Rouge-1 score is 0, Max Prob is 0.79, Avg Prob is 0.99, Max Ent is 0.86, Avg Ent is 0.94, Gb-S is 0.82, Wb-S is 0.83, Bb-S is 0.72, SU is 0.31, and Ask4-conf is 0. * **Answer 1:** Rouge-1 score is 0.67, Max Prob is 0.13, Avg Prob is 0.9, Max Ent is 0.82, Avg Ent is 0.9, Gb-S is 0.1, and Wb-S is 0.25. * **Answer 2:** Rouge-1 score is 0, Max Prob is 0, Avg Prob is 0.81, Max Ent is 0.7, Avg Ent is 0.82, Gb-S is 0.08, and Wb-S is 0.12. * **Answer 3:** Rouge-1 score is 0, Max Prob is 0, Avg Prob is 0.82, Max Ent is 0.86, Avg Ent is 0.89, Gb-S is 0.1, and Wb-S is 0.2. ### Key Observations * The "Ref answer" has the highest Rouge-1 score (1), indicating the best match with the reference. * The "Greedy answer" has a high Max Prob (0.79) and Avg Prob (0.99), but a Rouge-1 score of 0, suggesting it's confidently incorrect. * "Answer 1" has a relatively high Rouge-1 score (0.67) compared to "Answer 2" and "Answer 3". * "Answer 2" and "Answer 3" have Max Prob values of 0. ### Interpretation The data demonstrates a scenario where the language model (LLaMA2-7B) provides a "confidently wrong" answer. The "Greedy answer" has high probability scores (Max Prob and Avg Prob) but fails to match the reference answer (Rouge-1 score of 0). This suggests the model is confident in its incorrect answer. The other generated answers ("Answer 1", "Answer 2", "Answer 3") also show varying degrees of accuracy, with "Answer 1" being the closest to the reference based on the Rouge-1 score. The table highlights the importance of evaluating language models not only on probability scores but also on the accuracy of their responses.

\n ## Data Table: Confidently Wrong Answer Analysis (LLaMA2-7B) ### Overview This document presents an analysis of the responses generated by the LLaMA2-7B language model to a specific question. It compares the model's "greedy answer" and other potential answers to a reference answer, evaluating their similarity using several metrics. The document highlights a case where the model provides a confident but incorrect answer. ### Components/Axes The document consists of a textual description of the scenario, followed by a data table. The table has the following structure: * **Rows:** Represent different answers: "Ref answer" (reference answer), "Greedy answer" (the model's initial response), "Answer 1", "Answer 2", and "Answer 3". * **Columns:** Represent evaluation metrics: "Rogue-1", "Max Prob", "Avg Prob", "Max Ent", "Avg Ent", "Gb-S", "Wb-S", "Bb-S", "SU", and "Ask4-conf". The top section of the document provides the question and the answers. ### Content Details The question posed is: "Who had a 70s No 1 hit with Billy, Don't Be A Hero?" The reference answer is: "Bo Donaldson & The Heywoods". The greedy answer provided by the model is: "Paper Lace". Other answers considered are: * Answer 1: "Bo Donaldson" * Answer 2: "Paperchaser" * Answer 3: "Paper Moon" The data table contains the following numerical values (approximate, due to image quality): | Answer | Rogue-1 | Max Prob | Avg Prob | Max Ent | Avg Ent | Gb-S | Wb-S | Bb-S | SU | Ask4-conf | |---------------|---------|----------|----------|---------|---------|-------|-------|-------|-------|-----------| | Ref answer | 1 | 0.13 | 0.94 | 0.82 | 0.94 | 0.21 | 0.31 | | | | | Greedy answer | 0 | 0.79 | 0.99 | 0.86 | 0.94 | 0.82 | 0.83 | 0.72 | 0.31 | 0 | | Answer 1 | 0.67 | 0.13 | 0.9 | 0.82 | 0.9 | 0.1 | 0.25 | | | | | Answer 2 | 0 | 0 | 0.81 | 0.7 | 0.82 | 0.08 | 0.12 | | | | | Answer 3 | 0 | 0 | 0.82 | 0.86 | 0.89 | | 0.2 | | | | ### Key Observations * The "Greedy answer" has a high "Max Prob" (0.79) and "Avg Prob" (0.99), indicating the model was very confident in its response. * However, the "Rogue-1" score for the "Greedy answer" is 0, indicating no overlap with the reference answer. * "Answer 1" ("Bo Donaldson") has a Rogue-1 score of 0.67, suggesting it's the closest answer to the reference, despite having lower probabilities. * The "Ask4-conf" metric is 0 for all answers except the reference answer, which is not provided. ### Interpretation This document demonstrates a case of the LLaMA2-7B model exhibiting "hallucination" – generating a confident but factually incorrect answer. The high probability scores associated with the "Greedy answer" suggest the model is internally consistent but disconnected from the ground truth. The "Rogue-1" score serves as a critical indicator of factual accuracy, revealing the discrepancy between the model's confidence and correctness. The other answers show varying degrees of similarity to the correct answer, with "Answer 1" being the most plausible alternative. This example highlights the importance of evaluating language model outputs not only for fluency and coherence but also for factual accuracy, especially in applications where reliability is paramount. The metrics used (Rogue-1, probabilities, entropies) provide a quantitative framework for assessing these aspects.

## Data Table with Accompanying Text: Example of a Confidently Wrong Answer from LLaMA2-7B ### Overview The image is a figure, likely from a research paper or technical report, illustrating an example of a language model (LLaMA2-7B) providing a "confidently wrong" answer to a factual question. It presents the question, a reference answer, several model-generated answers, and a table of associated confidence and similarity metrics for each answer. ### Components/Axes The image is structured in three main vertical sections: 1. **Title (Top Center):** "An example of a confidently wrong answer (LM: LLaMA2-7B)" 2. **Question & Answer Block (Upper Section):** A beige, rounded rectangle containing: * **Question:** "Who had a 70s No 1 hit with Billy, Don't Be A Hero?" * **Ref answer:** "Bo Donaldson & The Heywoods" (displayed in blue text). 3. **Model Answers (Middle Section):** A list of answers generated by the model, each preceded by a small robot icon. * **Greedy answer:** "Paper Lace" (displayed in red text). * **Answer 1:** "Bo Donaldson" * **Answer 2:** "Paperchaser" * **Answer 3:** "Paper Moon" 4. **Data Table (Lower Section):** A table with 5 rows and 10 columns. The columns are: * (Row Label Column) * Rouge-1 * Max Prob * Avg Prob * Max Ent * Avg Ent * Gb-S * Wb-S * Bb-S * SU * Ask4-conf ### Detailed Analysis **Table Data Transcription:** The table provides quantitative metrics for each answer listed above. The rows correspond to the answers, and the columns to different evaluation metrics. | Row Label | Rouge-1 | Max Prob | Avg Prob | Max Ent | Avg Ent | Gb-S | Wb-S | Bb-S | SU | Ask4-conf | |----------------|---------|----------|----------|---------|---------|------|------|------|------|-----------| | **Ref answer** | 1 | 0.13 | 0.94 | 0.82 | 0.94 | 0.21 | 0.31 | | | | | **Greedy answer** | 0 | 0.79 | 0.99 | 0.86 | 0.94 | 0.82 | 0.83 | 0.72 | 0.31 | 0 | | **Answer 1** | 0.67 | 0.13 | 0.9 | 0.82 | 0.9 | 0.1 | 0.25 | | | | | **Answer 2** | 0 | 0 | 0.81 | 0.7 | 0.82 | 0.08 | 0.12 | | | | | **Answer 3** | 0 | 0 | 0.82 | 0.86 | 0.89 | 0.1 | 0.2 | | | | *Note: Empty cells in the table indicate no data was provided for that metric-answer combination.* **Key Metric Observations:** * **Rouge-1:** Measures n-gram overlap with the reference. The reference answer has a perfect score of 1. "Answer 1" ("Bo Donaldson") has a partial overlap (0.67). The "Greedy answer" and others have 0 overlap. * **Probability (Max/Avg Prob):** The "Greedy answer" has the highest maximum probability (0.79) and average probability (0.99), indicating the model assigned very high confidence to this incorrect token sequence. The reference answer has a much lower max probability (0.13). * **Entropy (Max/Avg Ent):** Entropy measures uncertainty. Values are relatively high across all answers (0.7 to 0.94), suggesting the model's internal state had significant uncertainty at the token level, even for the high-probability greedy answer. * **Similarity Scores (Gb-S, Wb-S, Bb-S, SU):** These are likely various semantic similarity metrics. The "Greedy answer" scores highest on Gb-S (0.82) and Wb-S (0.83), suggesting it is semantically similar to the reference in some embedding space, despite being factually wrong. "Answer 1" scores much lower on these metrics. * **Ask4-conf:** Only the "Greedy answer" has a value here (0), which may represent a specific confidence calibration metric. ### Key Observations 1. **Confident Error:** The "Greedy answer" ("Paper Lace") is factually incorrect but is generated with the highest model confidence (Max Prob 0.79, Avg Prob 0.99). 2. **Partial Correctness:** "Answer 1" ("Bo Donaldson") is partially correct (part of the reference answer) and has a moderate Rouge-1 score (0.67) but very low model confidence (Max Prob 0.13). 3. **Semantic Proximity of Wrong Answer:** The incorrect "Greedy answer" has high semantic similarity scores (Gb-S, Wb-S), indicating the model may have retrieved or generated a conceptually related but factually distinct entity ("Paper Lace" was another 70s band with a hit about a soldier). 4. **Metric Discrepancy:** There is a stark disconnect between the model's internal confidence metrics (high for the wrong answer) and factual accuracy (Rouge-1 of 0). ### Interpretation This figure demonstrates a critical failure mode in language models: **confident hallucination**. The model (LLaMA2-7B) selects "Paper Lace" as its top (greedy) answer with extremely high probability, despite it being wrong. The data suggests the model's decoding process prioritizes a semantically plausible and high-probability token sequence over factual correctness. The high semantic similarity scores for the wrong answer imply the model's internal representations place "Paper Lace" close to the correct answer "Bo Donaldson & The Heywoods" in vector space, likely due to shared context (1970s, music, Billboard hits). However, this proximity does not translate to factual accuracy. The low confidence for the partially correct "Answer 1" further shows the model fails to properly weight the correct factual components. This example underscores the limitation of relying solely on raw model probability or even semantic similarity for factual reliability. It highlights the need for techniques like retrieval augmentation, fact-checking modules, or improved training to align model confidence with truthfulness. The figure serves as a diagnostic tool, showing that a model can be simultaneously "right" in its semantic neighborhood and "wrong" in its specific factual output.

## Table: Model Answer Evaluation Metrics for Trivia Question ### Overview This table evaluates the performance of different answers to the question "Who had a 70s No.1 hit with Billy, Don't Be A Hero?" using multiple NLP metrics. The reference answer is "Bo Donaldson & The Heywoods," while the model's greedy answer ("Paper Lace") is incorrect. Three candidate answers are scored across 10 metrics. ### Components/Axes - **Rows**: 1. Reference answer ("Bo Donaldson & The Heywoods") 2. Greedy answer ("Paper Lace") 3. Answer 1 ("Bo Donaldson") 4. Answer 2 ("Paperchaser") 5. Answer 3 ("Paper Moon") - **Columns**: - Rouge-1 (rouge score) - Max Prob (maximum probability) - Avg Prob (average probability) - Max Ent (maximum entropy) - Avg Ent (average entropy) - Gb-S (grammaticality score) - Wb-S (word boundary score) - Bb-S (boundary bigram score) - SU (semantic unit score) - Ask4-conf (confidence in Ask4 metric) ### Detailed Analysis | Metric | Reference Answer | Greedy Answer | Answer 1 | Answer 2 | Answer 3 | |-----------------|------------------|---------------|----------------|----------------|----------------| | **Rouge-1** | 1.00 | 0.00 | 0.67 | 0.00 | 0.00 | | **Max Prob** | 0.13 | 0.79 | 0.13 | 0.00 | 0.00 | | **Avg Prob** | 0.94 | 0.99 | 0.90 | 0.81 | 0.82 | | **Max Ent** | 0.82 | 0.86 | 0.82 | 0.70 | 0.86 | | **Avg Ent** | 0.94 | 0.94 | 0.90 | 0.82 | 0.89 | | **Gb-S** | 0.21 | 0.82 | 0.10 | 0.08 | 0.10 | | **Wb-S** | 0.31 | 0.83 | 0.25 | 0.12 | 0.20 | | **Bb-S** | - | 0.72 | - | - | - | | **SU** | - | 0.31 | - | - | - | | **Ask4-conf** | - | 0.00 | - | - | - | ### Key Observations 1. **Reference Answer Dominance**: Scores perfectly on Rouge-1 (1.00) and shows high grammaticality (Wb-S: 0.31) despite lower probability metrics. 2. **Greedy Answer Failure**: - 0.00 Rouge-1 and Ask4-conf (confidently wrong) - High Avg Prob (0.99) but poor semantic alignment (SU: 0.31) 3. **Partial Matches**: - Answer 1 ("Bo Donaldson") shares 0.67 Rouge-1 with reference - Answer 3 ("Paper Moon") has highest Avg Prob (0.82) among incorrect answers 4. **Metric Discrepancies**: - Greedy answer has highest Max Prob (0.79) but lowest semantic scores - Answer 2 ("Paperchaser") shows worst grammaticality (Gb-S: 0.08) ### Interpretation The data reveals a critical failure mode in the LLaMA2-7B model: **high-confidence generation of semantically irrelevant answers**. While the greedy answer achieves high probability scores (Avg Prob: 0.99), it fails all semantic and grammaticality metrics, demonstrating a disconnect between statistical likelihood and factual accuracy. The reference answer's perfect Rouge-1 score (1.00) contrasts sharply with its lower probability metrics (Max Prob: 0.13), suggesting the model underestimates correct answers when they deviate from common associations. The partial matches (Answers 1 and 3) indicate the model can generate plausible-sounding but incorrect variants, with Answer 3 ("Paper Moon") being the most statistically favored incorrect option. This pattern highlights the need for confidence calibration and semantic grounding in large language models.