This paper investigates how Large Language Models (LLMs) internally represent the truthfulness of their generated text, focusing on the location of this information within the model's internal activations. The study analyzes various error detection methods, including probing classifiers applied to specific tokens within the LLM output, and compares their performance across different LLMs and datasets. The research reveals that truthfulness information is concentrated in specific tokens, improving error detection. Furthermore, the study explores the generalization of these methods across tasks, the predictability of error types, and the discrepancy between internal knowledge and generated answers. The findings shed light on the inner workings of LLMs and their limitations in generating truthful and accurate information.
Description: Figure 2 visually demonstrates the localized nature of truthfulness information by showing heatmaps of error detection performance across different layers and tokens for the Mistral-7b-instruct model. The heatmaps clearly show that the highest AUC scores are achieved at the exact answer tokens, confirming that these tokens hold the most information about the correctness of the LLM's output.
Relevance: This figure provides strong visual evidence for the key finding that truthfulness information is localized within specific tokens, supporting the argument for using exact answer tokens in error detection.
Description: Table 1 provides a quantitative comparison of different error detection techniques across various LLMs and datasets, using the AUC metric. The table clearly shows that probing classifiers applied to the exact answer tokens achieve the best performance compared to other methods, such as using aggregated logits or probabilities. The table includes numerical results (AUC scores) demonstrating the improved performance achieved by focusing on exact answer tokens.
Relevance: This table provides quantitative support for the paper's main claim by showing that probing classifiers applied to the exact answer tokens significantly outperform other error detection methods across multiple LLMs and datasets.
This paper demonstrates that LLMs encode truthfulness information within their internal representations, particularly in the "exact answer tokens." This finding enables improved error detection, especially in open-source models. However, the limited generalization of these truthfulness features across different tasks suggests the presence of skill-specific mechanisms within LLMs. The ability to predict error types and the observed discrepancy between internal knowledge and generated answers further highlight the complexity of LLM behavior. Future research should focus on exploring these skill-specific mechanisms, developing more robust error detection methods that generalize across tasks, and leveraging internal knowledge to improve LLM decoding strategies and mitigate the generation of incorrect information. This could involve developing adaptive probes that tailor their analysis to the specific task or designing new training procedures that encourage LLMs to better align their internal knowledge with their external behavior. Ultimately, these efforts will contribute to building more reliable and trustworthy LLMs for a wider range of applications.
Large language models (LLMs) often make errors, known as hallucinations. This paper shows that LLMs store information about the truthfulness of their answers, especially within specific tokens. This information can be used to detect errors more effectively, but these detectors don't work universally across different tasks. The paper also shows how to predict the types of errors LLMs are likely to make and reveals that sometimes LLMs internally know the correct answer but still generate a wrong one.
The abstract effectively summarizes the key findings and contributions of the paper in a concise and understandable manner.
The abstract clearly points out the novel aspects of the research, such as the token-specific encoding and the discrepancy between internal knowledge and external behavior.
The abstract concludes by suggesting potential future research directions based on the findings, encouraging further exploration in the field.
While the abstract mentions improved error detection, it would be beneficial to quantify this improvement (e.g., by stating the percentage increase in accuracy).
Rationale: Providing concrete numbers would strengthen the impact of the claim and give readers a better understanding of the practical significance of the findings.
Implementation: Include a specific metric or percentage improvement achieved by the proposed method.
The abstract could briefly mention the methodology used to analyze the internal representations (e.g., probing classifiers).
Rationale: This would provide readers with a better understanding of the technical approach and the nature of the analysis.
Implementation: Add a concise phrase or sentence describing the core technique used, such as "using probing classifiers trained on internal activations."
While the abstract focuses on error analysis and mitigation, it could briefly mention the broader implications of the findings for understanding LLM behavior and cognition.
Rationale: This would connect the research to a wider audience interested in the cognitive aspects of LLMs.
Implementation: Add a short phrase suggesting the broader implications, such as "These findings offer insights into the nature of LLM knowledge and reasoning."
Large language models (LLMs) are prone to generating incorrect information, often called "hallucinations." This paper shifts from focusing on how humans perceive these errors to examining how LLMs internally encode truthfulness. The research explores how this internal encoding can be used to better detect errors, predict error types, and potentially mitigate them. The paper also addresses the broad definition of "hallucinations" used in the study.
The introduction clearly establishes the motivation for the research by highlighting the limitations of existing user-centric approaches to understanding LLM hallucinations.
The introduction effectively emphasizes the novelty of the model-centric approach and its potential to provide deeper insights into LLM errors.
The introduction clearly defines the scope of the research, including the broad definition of hallucinations used and the focus on internal representations.
The introduction could benefit from a more explicit roadmap outlining the structure and key contributions of each section of the paper.
Rationale: A clear roadmap would improve the readability and help readers navigate the paper more effectively.
Implementation: Add a brief paragraph at the end of the introduction summarizing the content and purpose of each subsequent section.
Including a brief, illustrative example of an LLM hallucination and how the model-centric approach might be applied could enhance the introduction.
Rationale: A concrete example would make the concepts more accessible to readers and further motivate the research.
Implementation: Add a short example demonstrating an LLM error and how analyzing internal representations might reveal insights into its origin.
While the introduction mentions the limitations of user-centric approaches, it could further strengthen the motivation by connecting the research to the broader context of LLM development and deployment.
Rationale: Connecting the research to broader challenges in the field would increase its relevance and impact.
Implementation: Briefly discuss the implications of LLM hallucinations for real-world applications and the importance of developing robust error detection and mitigation techniques.
This section defines LLM errors, often called "hallucinations," and discusses existing research on detecting these errors. It emphasizes the lack of a universal definition for hallucinations and adopts a broad interpretation encompassing all types of LLM errors. The section also reviews prior work on error detection, including methods using external knowledge, output logits, and probing classifiers, highlighting the need for a more holistic approach.
The section provides a thorough overview of existing research on LLM hallucinations and error detection, covering various perspectives and approaches.
The section clearly defines the broad interpretation of hallucinations used in the paper and justifies this approach.
The section effectively highlights the shift from human-centric to model-centric analysis, emphasizing the importance of examining internal LLM activations.
While the section mentions various error types, providing more specific examples of these errors could enhance clarity and understanding.
Rationale: Concrete examples would make the different error types more tangible and relatable for the reader.
Implementation: Include specific examples of factual inaccuracies, biases, and reasoning failures in LLM outputs.
Given the later focus on probing classifiers, a more detailed explanation of their workings and limitations in this section would be beneficial.
Rationale: A more in-depth introduction to probing classifiers would prepare the reader for the subsequent sections and facilitate a better understanding of the methodology.
Implementation: Expand on the concept of probing classifiers, explaining how they are trained and used to analyze internal representations.
Explicitly stating the research questions addressed in the paper and how the background information relates to these questions would strengthen the section's focus.
Rationale: Connecting the background information to specific research questions would provide a clearer direction for the reader and enhance the overall coherence of the paper.
Implementation: State the research questions at the beginning or end of the section and link the discussed literature to these questions.
This section describes experiments on detecting errors in LLM-generated text. It focuses on how choosing the right token within the LLM's output significantly improves error detection. The section defines the task, explains the experimental setup (datasets, models, and metrics), and introduces the concept of using "exact answer tokens" for better error detection.
The section clearly defines the error detection task and its constraints, making the experimental setup easy to understand.
The section provides a detailed description of the experimental setup, including the datasets, models, and evaluation metric used.
The introduction of "exact answer tokens" is a novel approach that addresses a key limitation of existing methods and leads to improved error detection.
While the section lists the datasets used, it doesn't fully justify the selection. Explaining why these specific datasets were chosen and their relevance to the research question would strengthen the section.
Rationale: A stronger justification for the dataset selection would enhance the rigor and validity of the experiments.
Implementation: Provide a brief explanation of the criteria used for dataset selection and how each dataset contributes to the overall analysis.
Providing a concrete example of how exact answer tokens are identified in an LLM output would improve clarity.
Rationale: A concrete example would make the concept of exact answer tokens more tangible and easier to grasp.
Implementation: Include an example input prompt, LLM output, and the corresponding exact answer tokens.
The section could briefly discuss the limitations of using exact answer tokens, such as the potential difficulty in identifying these tokens automatically in real-world scenarios.
Rationale: Acknowledging the limitations would provide a more balanced perspective and highlight potential challenges for future research.
Implementation: Add a short paragraph discussing the potential challenges and limitations of the proposed approach.
Figure 1 provides examples of an input prompt and the LLM's response from the TriviaQA dataset. It highlights the specific tokens that can be probed for truthfulness information, including the first and last exact answer tokens within the generated response. This figure helps to visualize the concept of exact answer tokens and their position within the LLM's output.
Text: "Figure 1: Example for the input and LLM output from the TriviaQA dataset, and the names of the tokens that can be probed."
Context: Existing methods often overlook a critical nuance: the token selection for error detection, typically focusing on the last generated token or taking a mean. However, since LLMs typically generate long-form responses, this practice may miss crucial details (Brunner et al., 2020). Other approaches use the last token of the prompt (Slobodkin et al., 2023, inter alia), but this is inherently inaccurate due to LLMs’ unidirectional nature, failing to account for the generated response and missing cases where different sampled answers from the same model vary in correctness. We investigate a previously unexamined token location: the exact answer tokens, which represent the most meaningful parts of the generated response. We define exact answer tokens as those whose modification alters the answer’s correctness, disregarding subsequent generated content.3 Figure 1 illustrates the different token locations.
Relevance: This figure is relevant because it visually demonstrates the concept of 'exact answer tokens,' which are crucial for the proposed error detection method. By highlighting these tokens, the figure clarifies how the research pinpoints the locations within the LLM's output where truthfulness signals are strongest.
Figure 2 displays heatmaps showing the performance (AUC values) of a probe error detector across different layers and tokens of the Mistral-7b-instruct LLM. The heatmaps reveal that the error detection performance peaks at the exact answer tokens, particularly in the middle to later layers of the model. This visualization supports the claim that truthfulness information is concentrated in these specific tokens.
Text: "Figure 2: AUC values of a probe error detector across layers and tokens, Mistral-7b-instruct. Generation proceeds from left to right, with detection performance peaking at the exact answer tokens."
Context: Following prior work, we use a linear probing classifier for error detection (Li et al., 2024, inter alia) on static tokens: the last generated token (hl,−1), the one before it (hl,−2), and the final prompt token (hl,k). The layer l is selected per token based on validation set performance. For further details on the implementation of each method, refer to Appendix A.4. 3.3 EXACT ANSWER TOKENS Existing methods often overlook a critical nuance: the token selection for error detection, typically focusing on the last generated token or taking a mean. However, since LLMs typically generate long-form responses, this practice may miss crucial details (Brunner et al., 2020). Other approaches use the last token of the prompt (Slobodkin et al., 2023, inter alia), but this is inherently inaccurate due to LLMs’ unidirectional nature, failing to account for the generated response and missing cases where different sampled answers from the same model vary in correctness. We investigate a previously unexamined token location: the exact answer tokens, which represent the most meaningful parts of the generated response. We define exact answer tokens as those whose modification alters the answer’s correctness, disregarding subsequent generated content.3 Figure 1 illustrates the different token locations. In the following experiments, we implement each error detection method with an “exact answer” version, demonstrating that it often improves performance, especially in probing. These exact answer is identified from a lengthy generated answer using an external algorithm, which processes the question and the LLM’s response, A(qi, ˆyi), to extract the exact answer. In our implementation, we use Mistral-7b-Instruct in a few-shot learning setup as A. However, we demonstrate that all the LLMs we evaluate are capable of extracting exact answers from their own outputs, as explained in AppendixA.2. After extracting the exact answer, the exact answer tokens are identified through a simple search process. We focus on four specific tokens: the one immediately preceding the first exact answer token, the first exact answer token itself, the last exact answer token, and the one immediately following it. 3.4 RESULTS Patterns of truthfulness encoding. We first focus on probing classifiers to gain insights into the internal representations of LLMs. Specifically, we extensively analyze the effects of layer and token selection on activation extraction for these classifiers. This is done by systematically probing all layers of the model, starting with the last question token and continuing through to the final generated token. Figure 2 shows the AUC metrics of trained probes across various layers and tokens of Mistral-7b-Instruct. While some datasets seem easier for error prediction, all exhibit consistent truthfulness encoding patterns.
Relevance: Figure 2 directly supports a central claim of the paper: that truthfulness information is concentrated in the exact answer tokens. The heatmaps visually demonstrate the peak performance of the error detector at these tokens, providing strong evidence for this claim.
Table 1 compares the performance of various error detection techniques across different Large Language Models (LLMs) and datasets, using the Area Under the Curve (AUC) metric. The techniques include simple baselines like 'Majority' and more sophisticated methods like 'Logits-mean', 'Logits-min', 'p(True)', and 'Probe'. The table also shows the impact of using exact answer tokens on the performance of these techniques. The best-performing method for each LLM-dataset combination is highlighted in bold.
Text: "Table 1: Comparison of error detection techniques using AUC metric, across different models and datasets. The best-performing method is bolded. Using exact answer tokens is useful for many cases, especially probing."
Context: Next, we evaluate various error detection methods by comparing their performance with and without the use of exact answer tokens. Table 1 compares the AUC across three representative datasets (additional datasets and models in Appendix B, showing consistent patterns). Here we present results for the last exact answer token, which outperformed both the first exact answer token and the one preceding it, while the token following the last performed similarly.
Relevance: This table is crucial for understanding the effectiveness of different error detection methods and the impact of using exact answer tokens. It directly addresses the paper's main contribution of improving error detection by focusing on specific tokens.
Table 5 presents a comparison of error detection performance, measured by AUC, on the Mistral-7B-Instruct model. Various methods are compared, including majority voting, logit-based methods (mean, min, with and without exact answer tokens), probability-based methods, p(True), and probing at different token positions. The table covers several datasets, including TriviaQA, Winobias, Math, Movies, IMDB, HotpotQA, HotpotQA-WC, Winogrande, NLI, and NQ-WC.
Text: "Table 5: Comparison of error detection performance (AUC) on Mistral-7B-Instruct."
Context: In Table 8 we present some qualitative samples from Mistral-7b-instruct, for the phenomenon we observe at error type (C2) Consistently incorrect but generates the correct answer at least one time. The samples in the table represent cases where the probe chose the correct answer. Table 9 compares different decoding mechanisms, including the choice via probe, on non-instruct models, and Table 10 compares on the instruct models.
Relevance: This table provides a comprehensive overview of the error detection performance of the Mistral-7B-Instruct model across various methods and datasets, allowing for a direct comparison of their effectiveness.
Table 6 presents a comparison of error detection performance, measured by the Area Under the Curve (AUC) score, for the Llama-8b language model. The table is structured to compare various error detection methods across different datasets, including TriviaQA, Winobias, Math, Movies, IMDB, HotpotQA, HotpotQA-WC, Winogrande, NLI, and NQ-WC. The methods compared include a majority baseline, logits-based methods (with and without exact answer tokens), probabilities-based methods, p(True) methods, and probing at different token positions (last generated, before last generated, end of question, exact answer last, and exact answer last+1). Each cell in the table provides the AUC score and its standard deviation for a specific method and dataset combination.
Text: "Table 6: Comparison of error detection performance (AUC) on Llama-8b."
Context: The caption is located above Table 6 on page 26.
Relevance: This table is highly relevant as it provides a comprehensive comparison of different error detection methods on the Llama-8b model. It allows for direct comparison of the effectiveness of various techniques and highlights the impact of using exact answer tokens. The results contribute to understanding the strengths and weaknesses of each method and inform the choice of appropriate error detection strategies for different datasets and tasks.
Table 7 provides a comparison of error detection performance, using the AUC metric, for the Llama-8b-Instruct model. It compares various error detection methods across multiple datasets, including TriviaQA, Winobias, Math, Movies, IMDB, HotpotQA, HotpotQA-WC, Winogrande, NLI, and NQ-WC. The methods compared include a majority baseline, logits-based methods (with and without exact answer tokens), probabilities-based methods, p(True) methods, and probing at different token positions. Each cell presents the AUC score and its standard deviation for a specific method and dataset.
Text: "Table 7: Comparison of error detection performance (AUC) on Llama-8b-Instruct."
Context: The caption is above Table 7 on page 27.
Relevance: This table is crucial for understanding the performance of different error detection techniques on the Llama-8b-Instruct model. It allows for direct comparison of the methods and shows the effect of using exact answer tokens. The results contribute to evaluating the effectiveness of each method and inform the selection of suitable error detection strategies for different datasets and tasks.
This section investigates how well error detection models, specifically probing classifiers, generalize across different tasks. While initial results suggest some generalization, further analysis reveals that this is mostly due to information already present in the output logits. True generalization, beyond what logits can capture, is limited to tasks requiring similar skills, like factual recall. This suggests LLMs have multiple, task-specific ways of encoding truthfulness, not a single universal mechanism.
The section clearly states the research question regarding the generalization of probing classifiers across tasks, providing a clear focus for the analysis.
The section presents a thorough analysis of the generalization performance, including both raw AUC scores and the performance difference compared to a logit-based baseline.
The section provides an insightful interpretation of the results, connecting the limited generalization to the concept of skill-specific truthfulness mechanisms.
While the experiments cover a range of tasks, exploring additional, more diverse tasks could further strengthen the conclusions about skill-specific generalization.
Rationale: Including a wider variety of tasks would provide a more comprehensive understanding of the limits of generalization and the nature of skill-specific truthfulness.
Implementation: Consider adding tasks that involve different types of reasoning, language understanding, or knowledge domains.
The section mentions unexplained asymmetric generalization patterns (e.g., TriviaQA to Math). Further investigation into these patterns could reveal interesting insights.
Rationale: Understanding the reasons behind asymmetric generalization could shed light on the relationships between different skills and truthfulness mechanisms.
Implementation: Analyze the specific features or representations that contribute to the asymmetric generalization and explore potential explanations.
The section could further elaborate on the practical implications of the findings for developing and deploying error detection systems in real-world applications.
Rationale: A more detailed discussion of the practical implications would increase the relevance of the research for practitioners and guide future development of error detection systems.
Implementation: Discuss how the findings could inform the design and training of more robust and adaptable error detectors.
Figure 3 presents two heatmaps illustrating the generalization performance of the Mistral-7b-instruct model across different datasets. Heatmap (a) shows the raw AUC values when a probe trained on one dataset is tested on another. Values above 0.5 suggest some generalization. Heatmap (b) shows the difference in AUC between the probe and a logit-based method. Positive values indicate that the probe generalizes better than simply using logits, suggesting the probe learns something beyond what's available in the output probabilities.
Text: "Figure 3: Generalization between datasets, Mistral-7b-instruct. After subtracting the logit-based method's performance, we observe that most datasets show limited or no meaningful generalization."
Context: Results. Figure 3a shows the generalization results for Mistral-7b-instruct, with similar patterns observed for other LLMs in Appendix C. In this context, values above 0.5 indicate successful generalization. At first glance, the results appear consistent with previous research: most heatmap values exceed 0.5, implying some degree of generalization across tasks. This observation supports the existence of a universal mechanism for decoding truthfulness, since the same linear directions—captured by the probe—encode truthfulness information across many datasets. However, upon closer inspection, it turns out that most of this performance can be achieved by logit-based truthfulness detection, which only observes the output logits. Figure 3b presents the same heatmap after subtracting results from our strongest logit-based baseline (Logit-min-exact). This adjusted heatmap reveals the probe’s generalization rarely exceeds what can be achieved by examining logits alone. This implies that the apparent generalization does not stem from a universal internal encoding of truthfulness but rather reflects information already accessible through external features like logits.
Relevance: This figure is central to the paper's argument about the limitations of generalization in error detection. It shows that while some generalization appears to occur, it's mostly explained by information already present in the output logits, challenging the idea of a universal truthfulness encoding.
This section explores the different types of errors LLMs make, focusing on the TriviaQA dataset. It introduces a taxonomy of errors based on how consistently the LLM generates correct or incorrect answers when prompted repeatedly. The section then investigates whether the LLM's internal representations can predict these error types.
The introduction of a taxonomy based on repeated sampling provides a new perspective on LLM errors, moving beyond simple binary classifications of correct/incorrect.
The illustrative examples in Figure 4 effectively demonstrate the different error types and the rationale behind the taxonomy.
The investigation into predicting error types from internal representations provides valuable insights into the LLM's internal workings and potential error mechanisms.
While the focus on TriviaQA is understandable, applying the error taxonomy and prediction analysis to other datasets would strengthen the generalizability of the findings.
Rationale: Analyzing error types in different datasets would reveal whether the taxonomy and prediction capabilities hold across various tasks and error types.
Implementation: Apply the same methodology to other datasets used in the paper, such as Winobias, Math, or NLI.
The section mentions that the taxonomy covers 96% of errors in TriviaQA for Mistral-7b-instruct. Providing similar statistics for other models and datasets would be informative.
Rationale: Quantifying the coverage of the taxonomy across different models and datasets would help assess its comprehensiveness and identify potential gaps.
Implementation: Calculate and report the percentage of errors covered by the taxonomy for each model and dataset combination.
The section acknowledges some overlap between error types. A more detailed discussion of the limitations of the taxonomy and potential areas for improvement would be beneficial.
Rationale: A critical discussion of the taxonomy's limitations would enhance the rigor of the analysis and provide directions for future refinement.
Implementation: Elaborate on the specific areas of overlap and discuss potential ways to address these ambiguities in the taxonomy.
Figure 4 illustrates three distinct error types observed in the free-form generation of a large language model (LLM) when the same question is sampled multiple times. Each panel (a, b, and c) depicts a different scenario. Panel (a) shows a case where the LLM mostly generates the correct answer but occasionally produces incorrect ones (hallucinations). Panel (b) shows a case where the LLM mostly generates the same incorrect answer, but occasionally produces the correct one, suggesting some underlying knowledge of the correct answer. Panel (c) shows a case where the LLM generates many different answers, with the correct answer appearing infrequently, indicating low confidence and high variability in its responses.
Text: "Figure 4: Different error types in free-form generation, exposed when resampled many times."
Context: To analyze errors from the LLM’s perspective, we sample K = 30 responses at a temperature setting of T = 14 for each example in the dataset and then analyze the resulting distribution of answers. Figure 4 illustrates three representative error types. In one (Figure 4a), the model usually gives the correct answer but occasionally make an error, implying correct information is present but sampling may lead to mistakes. In another (Figure 4b, the model often responds incorrectly, though it is capable of providing the right answer, indicating some retained knowledge despite consistently making the same error. In a third type (Figure 4c), the model generates a wide array of mostly incorrect answers, reflecting low confidence in any generated answer.
Relevance: This figure is crucial for understanding the different ways LLMs can make errors. It moves beyond simply labeling an answer as correct or incorrect and delves into the patterns of errors, which is essential for developing targeted mitigation strategies.
Table 2 presents the AUC scores for classifying different error types using the internal representations of four LLMs: Mistral-7b, Mistral-Instr-7b, Llama3-8b, and Llama3-Instr-8b. The error types include (A) Refuses to answer, (B) Consistently correct, (C) Consistently incorrect, (D) Two competing, and (E) Many answers. The AUC scores, along with their standard deviations, indicate how well the models' internal representations can predict these error types. Higher AUC scores suggest better predictability.
Text: "Table 2: AUC scores for error type classification. Error types are predictable from the inner model representations, indicating the encoding of fine-grained information on errors."
Context: Our taxonomy offers an external, behavioral analysis of LLMs, which we complement by an intrinsic evaluation. We explore whether LLMs encode information on potential error types within their intermediate activations, offering a deeper insight into the underlying mechanisms. To investigate this, we train a probe in a one-to-many setting, where a single probe identifies a specific error type from all others. We use representations extracted from the answers produced via greedy decoding. Table 2 presents the test set results for all models. Our findings demonstrate that the error type can be predicted from the intermediate representations of the greedy decoding generations, suggesting that they may encode not just output correctness but also features that are correlative with fine-grained information about potential errors. This predictability opens up possibilities for targeted interventions on specific error types.
Relevance: This table directly supports the claim that LLMs encode information about the types of errors they are likely to make. The AUC scores demonstrate that error types are predictable from internal representations, suggesting a link between internal states and external behavior.
This section explores whether the internal signals of LLMs about correctness align with their actual generated answers. By using a "probe" trained to detect errors, the researchers select answers from a pool of generated responses and compare the accuracy of this probe-based selection to traditional methods like greedy decoding. The results show that the probe significantly improves accuracy, especially when the LLM doesn't consistently generate the correct answer, suggesting a disconnect between the LLM's internal knowledge and its external behavior.
Using a probe trained on error detection for answer selection is a novel approach that provides a unique way to investigate the internal-external alignment of LLMs.
Focusing the analysis on different error types provides valuable insights into the specific scenarios where the probe is most effective and highlights the disconnect between internal knowledge and external behavior.
The results clearly demonstrate the improvement in accuracy achieved by the probe-based selection and suggest potential directions for leveraging internal knowledge to reduce errors.
The section uses a probe trained on error detection. Exploring different probe training objectives (e.g., directly predicting answer correctness) could provide further insights.
Rationale: Different training objectives might lead to different answer selection strategies and reveal different aspects of the LLM's internal representations.
Implementation: Train probes with different objectives, such as predicting answer correctness or ranking candidate answers, and compare their performance in answer selection.
The section focuses on the accuracy improvement. Analyzing the probe's behavior (e.g., which features it relies on, which answers it selects) could provide a deeper understanding of its effectiveness.
Rationale: Understanding how the probe selects answers could reveal the specific internal representations that contribute to improved accuracy and provide insights into the LLM's decision-making process.
Implementation: Analyze the probe's activations, attention patterns, or other relevant features to understand its answer selection strategy.
While the section mentions the probe as a diagnostic tool, it could further discuss potential practical applications of the findings for error mitigation or improvement of LLM decoding strategies.
Rationale: Discussing potential practical applications would increase the impact of the research and provide directions for future work.
Implementation: Explore how the probe-based selection method could be incorporated into existing LLM decoding strategies or used to develop new error mitigation techniques.
Figure 5 presents two bar charts comparing the accuracy of different answer selection strategies for the Mistral-7B-Instruct model on two datasets: (a) TriviaQA and (b) Math. The strategies compared are: greedy decoding (taking the first generated answer), random selection, selecting the most frequent answer (majority vote), and selecting the answer with the highest probability according to a probe trained to detect correct answers. The bars are grouped by error types, which categorize the model's behavior across multiple generations of the same question (e.g., consistently correct, consistently incorrect, two competing answers, many answers). The figure highlights that using the probe to select answers leads to significant accuracy improvements, especially for error types where the model doesn't show a clear preference for the correct answer across multiple generations.
Text: "Figure 5: Different answer choice strategies, Mistral-7B-Instruct. A notable improvement in accuracy is observed for error types where the LLM shows no preference for the correct answer across repeated generations."
Context: Results The results for Mistral-7b-instruct are summarized in Figure 5, with additional results for other LLMs and datasets as well as qualitative examples provided in Appendix E. We only present results on error types that appear 30 times or more in our test dataset. Overall, using the probe to select answers enhances the LLMs accuracy across all examined tasks. However, the extent of improvement varies by error type. For instance, in the TriviaQA dataset, there is minimal gain in the “mostly correct” category (B2). In contrast, substantial gains—ranging from 30 to 40 points in some cases—are observed in the “mostly incorrect” (C2), “two competing answers” (D), and “many answers” (E1) categories. Interestingly, and perhaps surprisingly, the probe is most effective in cases where the LLM lacks any (external) preference for the correct answer during generation. The fact that the probe can effectively identify the correct answer in these scenarios, points at a significant disconnect between the LLM’s internal encoding and its external behavior. These results suggest that even when the model encodes information of which answer is correct, it can still generate an incorrect answer in practice.
Relevance: This figure is highly relevant as it directly addresses the research question of whether the LLM's internal knowledge of truthfulness aligns with its external behavior (answer generation). It shows that using a probe based on internal representations can significantly improve answer selection, especially when the model's generation behavior is inconsistent or incorrect, revealing a disconnect between internal knowledge and external behavior.
This section summarizes the paper's findings, highlighting the localized nature of truthfulness information within exact answer tokens. This improves error detection, particularly in open-source LLMs. The study also reveals that truthfulness features don't generalize well across different tasks, suggesting skill-specific mechanisms. Furthermore, LLMs can often predict their own error types, and there's a discrepancy between internal knowledge and generated answers, where LLMs might internally know the correct answer but still generate an incorrect one. The paper concludes by emphasizing the value of analyzing internal representations for understanding and mitigating LLM errors.
The section effectively summarizes the key findings of the paper in a clear and concise manner, highlighting the most important contributions.
The section discusses the practical implications of the findings, such as improved error detection and the potential for developing targeted mitigation strategies.
The section points out areas for future research, such as investigating the factors influencing generalization and leveraging internal knowledge for error reduction.
The section mentions the limitation of requiring access to internal representations, primarily affecting open-source models. Elaborating on this limitation and potential solutions for closed-source models would be beneficial.
Rationale: This would address a key practical concern and broaden the applicability of the findings.
Implementation: Discuss potential techniques for analyzing closed-source models, such as using API calls or developing methods that rely on external behavior.
The section states that truthfulness features generalize poorly across tasks. Quantifying this poor generalization with specific metrics or examples would strengthen the claim.
Rationale: Providing concrete evidence of poor generalization would make the argument more convincing.
Implementation: Include specific examples of cross-task performance or report the drop in accuracy when applying a model trained on one task to another.
The section mentions a discrepancy between internal knowledge and generated answers. Providing more details about this discrepancy and potential mitigation strategies would be valuable.
Rationale: This would provide a deeper understanding of the issue and potential avenues for improving LLM performance.
Implementation: Discuss possible reasons for the discrepancy, such as training objectives or decoding strategies, and suggest concrete mitigation techniques, such as modifying the training process or using different decoding methods.
This appendix details the implementation of the error detection methods, including how errors were identified, how the probing classifiers were implemented, the datasets used, and the baseline methods. This information is crucial for reproducing the study's results and understanding the methodology.
The section provides a clear explanation of the probing methodology, including the choice of MLP output for analysis and the use of logistic regression.
The section clearly describes how the correctness labels were obtained for the probing dataset, including the use of heuristics and an instruct LLM for validation.
The section justifies the method used for extracting exact answer tokens and explains the steps taken to avoid bias in the probing task.
The section mentions using logistic regression but doesn't provide details on hyperparameter tuning. Explaining how hyperparameters were chosen would improve reproducibility.
Rationale: Providing details on hyperparameter tuning would allow others to replicate the experiments more accurately and ensure a fair comparison.
Implementation: Include information on the hyperparameters used (e.g., regularization strength) and the method used for tuning (e.g., cross-validation).
While the section mentions using 10K training and test samples, it's unclear how these splits were created. Providing more details on the splitting process would enhance reproducibility.
Rationale: A clear description of the dataset splitting process is essential for reproducibility and ensures that the results are not influenced by the specific split used.
Implementation: Specify whether random splitting or a predefined split was used. If random splitting was used, provide the seed used for randomization.
The section mentions using different prompts for different datasets and LLMs but doesn't provide specific examples. Including examples of the prompts used would improve clarity and reproducibility.
Rationale: Providing examples of the prompts would allow others to understand the specific instructions given to the LLMs and replicate the experiments more accurately.
Implementation: Include a few representative examples of the prompts used for different datasets and LLMs in the appendix.
Table 3 shows the success rate of different large language models (LLMs) in extracting the exact answer from their own generated long-form answers. It lists four models (Mistral-7b, Mistral-Instruct-7b, Llama3-8b, and Llama3-Instruct-8b) and their respective success rates. This demonstrates that LLMs can, to a large extent, identify the key information within their own outputs, even if they don't always present it as the final, concise answer.
Text: "Table 3: Success rate of extracting exact answer from a long model answer. Each model is used to extract answers from its own output."
Context: To avoid bias in our probing task, we only retain questions for which a valid exact answer was successfully extracted. This ensures there is no unfair correlation between invalid answers and incorrect answers in the experiments. We note the following: (a) While it is possible to use an instruct LLM to extract every answer regardless of its correctness, we chose the aforementioned strategy to improve the efficiency of our experiments; (b) This is just one possible implementation. For each LLM, one could use the same LLM to extract its own exact answer token, as demonstrated in a proof-of-concept over 1000 samples of TriviaQA in Table 3. Alternatively, it may be more efficient to train a smaller system specifically designed for detecting exact answer tokens, which would be more suitable for real-world scenarios. We choose to keep the extraction process as abstract as possible, as our primary focus is not on the specific implementation, but on analyzing the potential gains from probing these locations. Additionally, if the exact answer token is not among the first generated tokens, we examine the token immediately preceding it (“before exact answer token”). If the exact answer token is not the last one, we also examine the following token. When the exact answer spans multiple tokens, the first and last exact answer tokens are probed separately.
Relevance: Table 3 is relevant because it demonstrates the feasibility of automatically extracting the exact answer from an LLM's output. This extraction process is crucial for the proposed method of probing exact answer tokens for truthfulness signals. The high success rates shown in the table validate the use of this approach.
This appendix provides the complete error detection results, complementing the findings presented in the main paper. It includes figures showing the performance of the probe across different layers and tokens for various datasets and models, as well as tables with detailed results for all error detection methods and datasets. These results support the main paper's conclusions.
The appendix provides a complete set of results, allowing readers to thoroughly examine the data and verify the claims made in the main paper.
The inclusion of both visualizations (Figure 6) and tables (Tables 4-7) provides multiple ways to understand the data and facilitates a deeper analysis.
While the tables provide detailed results, adding aggregated statistics (e.g., average AUC across datasets for each method) would make it easier to compare overall performance.
Rationale: Aggregated statistics would provide a high-level overview of the performance and facilitate comparisons between different methods.
Implementation: Add a separate table or section summarizing the average AUC scores and other relevant statistics across datasets for each error detection method.
While the appendix states that the results are consistent with the main paper, it would be beneficial to explicitly discuss any inconsistencies or unexpected results and provide potential explanations.
Rationale: Addressing any discrepancies would enhance the transparency and credibility of the analysis.
Implementation: Add a section discussing any deviations from the expected patterns and provide potential explanations or interpretations.
The appendix mentions Figure 6 and Tables 4-7 but only shows a portion of Figure 6. Providing access to the full results, either within the appendix or through a supplementary material link, would be essential for reproducibility and further analysis.
Rationale: Providing access to the complete results is crucial for transparency and allows other researchers to verify the findings and build upon the work.
Implementation: Include the full Figure 6 and Tables 4-7 in the appendix or provide a clear link to supplementary material where these results can be accessed.
Figure 6 presents heatmaps visualizing the Area Under the Curve (AUC) values of a trained probe across different layers and tokens for the Mistral-7b-instruct model. Each heatmap corresponds to a different dataset (HotpotQA, HotpotQA with context, Movies, Winogrande, NLI, IMDB). The x-axis represents the tokens, with the exact answer tokens highlighted. The y-axis represents the model's layers. The color intensity indicates the AUC value, with darker blue representing higher AUC and thus better error detection performance. The figure demonstrates that the error detection performance peaks around the exact answer tokens, particularly in the middle to later layers of the model, similar to the pattern observed in Figure 2 for TriviaQA, Winobias, and Math.
Text: "Figure 6 presents the AUC values of a traind probe across layers and token for Mistral-7b-instruct, showing a similar pattern across all datasets. We also observe similar patterns across other models. See our repo https://github.com/technion-cs-nlp/LLMsKnow for the figures."
Context: B FULL ERROR DETECTION RESULTS Figure 6 presents the AUC values of a traind probe across layers and token for Mistral-7b-instruct, showing a similar pattern across all datasets. We also observe similar patterns across other models. See our repo https://github.com/technion-cs-nlp/LLMsKnowfor the figures. Tables 4, 5, 6, and 7 present the full error detection results across all baselines and datasets, which are consistent with the main paper results.
Relevance: Figure 6 expands upon the findings presented in Figure 2 by showing that the pattern of peak error detection performance at the exact answer tokens holds across a wider range of datasets. This reinforces the paper's central argument about the localized nature of truthfulness information within LLMs.
Table 4 compares the performance (AUC) of various error detection methods on the Mistral-7B model across ten datasets. The methods include simple strategies like always predicting the majority class, using the mean or minimum of logits or probabilities (with and without considering the 'exact' answer tokens), prompting the model to assess its own truthfulness ('p(True)'), and using probing classifiers at different token locations. The table shows that probing the exact answer token generally yields the highest AUC scores, indicating better error detection performance.
Text: "Tables 4, 5, 6, and 7 present the full error detection results across all baselines and datasets, which are consistent with the main paper results."
Context: B FULL ERROR DETECTION RESULTS Figure 6 presents the AUC values of a traind probe across layers and token for Mistral-7b-instruct, showing a similar pattern across all datasets. We also observe similar patterns across other models. See our repo https://github.com/technion-cs-nlp/LLMsKnow for the figures. Tables 4, 5, 6, and 7 present the full error detection results across all baselines and datasets, which are consistent with the main paper results.
Relevance: Table 4 provides comprehensive results supporting the paper's claim that focusing on exact answer tokens improves error detection. It compares various methods, including established baselines and the proposed probing technique, demonstrating the latter's superior performance across multiple datasets.
Table 5, similar to Table 4, presents a comparison of error detection performance (AUC) but for the Mistral-7B-Instruct model. It evaluates various methods across the same ten datasets, including majority voting, logits and probabilities (with mean, min, max, and exact answer variations), p(True), and probing at different token positions. The results consistently show that probing, especially at the exact answer token, often outperforms other methods.
Text: "Tables 4, 5, 6, and 7 present the full error detection results across all baselines and datasets, which are consistent with the main paper results."
Context: Same as Table 4's first mention.
Relevance: Table 5 provides further evidence supporting the paper's main claim by demonstrating the effectiveness of probing exact answer tokens for error detection on an instructed version of the LLM. This shows that the method's benefits extend beyond the base model to a more refined, instruction-tuned version.
Table 6 presents the error detection performance (AUC) for the Llama-8b model, similar in structure to Tables 4 and 5. It compares various methods, including majority voting, logits and probabilities (with and without exact answer tokens), p(True), and probing at different token positions, across the same ten datasets. The results generally show that probing at the exact answer token yields the best performance.
Text: "Tables 4, 5, 6, and 7 present the full error detection results across all baselines and datasets, which are consistent with the main paper results."
Context: Same as Table 4's first mention.
Relevance: Table 6 extends the analysis to a different LLM architecture (Llama-8b), demonstrating that the benefits of probing exact answer tokens for error detection are not limited to a specific model (Mistral). This strengthens the generalizability of the findings.
Table 7 presents the error detection performance (AUC) for the Llama-8b-Instruct model, mirroring the structure of the previous tables. It compares various methods, including majority voting, logits and probabilities (with and without exact answer tokens), p(True), and probing at different token positions, across ten datasets. The results generally indicate that probing at the exact answer token leads to the best error detection performance.
Text: "Tables 4, 5, 6, and 7 present the full error detection results across all baselines and datasets, which are consistent with the main paper results."
Context: Same as Table 4's first mention.
Relevance: Table 7 completes the comprehensive analysis by presenting results for the instruction-tuned version of the Llama model. This demonstrates the consistency of the findings across both base and instructed versions of two different LLM architectures (Mistral and Llama), further strengthening the generalizability of the paper's claims.
Figure 6 presents heatmaps visualizing the performance of a probe error detector across different layers and tokens for the Mistral-7b-instruct model. Each heatmap corresponds to a different dataset (HotpotQA, HotpotQA with context, Movies, Winogrande, NLI, IMDB). The x-axis represents the tokens, with the exact answer tokens highlighted. The y-axis represents the model's layers. The color intensity indicates the AUC value, with darker blue representing higher AUC and thus better error detection performance. The key observation is that the detection performance spikes at the exact answer tokens, supporting the idea that truthfulness information is concentrated there.
Text: "Figure 6: AUC values of a probe error detector across layers and tokens, Mistral-7b-instruct. The detection performance spikes at the exact answer tokens."
Context: B FULL ERROR DETECTION RESULTS Figure 6 presents the AUC values of a traind probe across layers and token for Mistral-7b-instruct, showing a similar pattern across all datasets. We also observe similar patterns across other models. See our repo https://github.com/technion-cs-nlp/LLMsKnowfor the figures. Tables 4, 5, 6, and 7 present the full error detection results across all baselines and datasets, which are consistent with the main paper results.
Relevance: Figure 6 is highly relevant as it visually demonstrates the core finding of the paper: that truthfulness information is concentrated in the exact answer tokens. The heatmaps show a clear spike in error detection performance (AUC) at these tokens across various datasets, providing strong evidence for this claim.
Table 4 compares the performance (AUC) of various error detection methods on the Mistral-7B model across ten different datasets. The methods include simple strategies like always predicting the majority class, using the mean, minimum, or maximum of logits or probabilities, prompting the model to assess its own truthfulness ('p(True)'), and using probing classifiers at various token positions. The table shows that probing the exact answer tokens generally yields the highest AUC scores, indicating superior error detection performance.
Text: "Table 4: Comparison of error detection performance (AUC) on Mistral-7B."
Context: B FULL ERROR DETECTION RESULTS Figure 6 presents the AUC values of a traind probe across layers and token for Mistral-7b-instruct, showing a similar pattern across all datasets. We also observe similar patterns across other models. See our repo https://github.com/technion-cs-nlp/LLMsKnowfor the figures. Tables 4, 5, 6, and 7 present the full error detection results across all baselines and datasets, which are consistent with the main paper results.
Relevance: Table 4 provides a comprehensive comparison of different error detection methods, demonstrating the effectiveness of the proposed approach (probing exact answer tokens) compared to existing baselines. This table supports the central claim of improved error detection.
This appendix provides the full set of results for the generalization experiments, complementing the analysis in the main paper. It includes heatmaps showing the generalization performance of three LLMs (Mistral-7b, Llama-3-8b, and Llama-3-8b-instruct) across different datasets. The heatmaps show both raw AUC values and the performance difference between the probe method and a logit-based baseline. These results further support the paper's findings about the limited generalization of truthfulness features across tasks.
The appendix provides a complete set of generalization results for multiple LLMs, allowing for a thorough examination of the data and supporting the main paper's analysis.
The use of heatmaps effectively visualizes the generalization performance across different datasets and models, making it easy to identify patterns and trends.
While the appendix mentions notable differences between models, it would be beneficial to elaborate on these differences and provide potential explanations.
Rationale: Discussing model-specific differences would provide a deeper understanding of the factors influencing generalization and the varying ways in which LLMs encode truthfulness.
Implementation: Analyze the specific patterns observed in each model's heatmaps and discuss potential reasons for the differences, considering factors such as model architecture, training data, or size.
The appendix could explicitly connect the observed generalization patterns to the concept of skill-specific truthfulness discussed in the main paper.
Rationale: Connecting the results to the skill-specific generalization concept would strengthen the overall coherence of the paper and provide a more unified interpretation of the findings.
Implementation: Discuss how the observed generalization patterns support or challenge the idea of skill-specific truthfulness and elaborate on the implications for error detection and mitigation.
The heatmaps show raw AUC values and differences. Adding statistical significance measures (e.g., p-values or confidence intervals) would strengthen the analysis and make the results more robust.
Rationale: Statistical significance measures would provide a more rigorous assessment of the observed differences in generalization performance and help determine whether the patterns are statistically significant or due to random variation.
Implementation: Calculate and report p-values or confidence intervals for the AUC values and differences shown in the heatmaps.
Figure 7 illustrates the generalization capabilities of the Mistral-7b model across different datasets using two heatmaps. Heatmap (a) displays raw AUC values, where values above 0.5 suggest some level of generalization. Heatmap (b) shows the difference in AUC scores between the probe method and a logit-based method. Positive values in heatmap (b) indicate that the probe generalizes better than the logit-based method, learning additional information beyond what is captured by the output logits. The datasets used for both training and testing include TriviaQA, HotpotQA, Movies, Winobias, Winogrande, NLI, IMDB, Math, HotpotQA with context (WC), and Natural Questions with context (WC).
Text: "Figure 7: Generalization between datasets, Mistral-7b."
Context: Figures 7, 8 and 9 present the generalization results for the remaining models. While these results exhibit similar high-level patterns to those found in the main paper on Mistral-7b-instruct, notable differences suggest that these models may possess different mechanisms for encoding truthfulness.
Relevance: Figure 7 is relevant because it provides a visual representation of how well the error detection capabilities of Mistral-7b generalize across different tasks. It helps to understand whether the model has a universal truthfulness mechanism or if it's task-specific. This is important for determining the practical applicability of error detection methods.
Figure 8, similar to Figure 7, presents the generalization performance but for the Llama-3-8b model. It uses two heatmaps: (a) shows the raw AUC values, where values above 0.5 suggest some generalization across datasets, and (b) shows the difference in AUC between the probe method and a logit-based method. Positive values in (b) indicate that the probe learns information beyond what's captured by the logits. The same datasets are used for training and testing as in Figure 7: TriviaQA, HotpotQA, Movies, Winobias, Winogrande, NLI, IMDB, Math, HotpotQA WC, and NQ WC.
Text: "Figure 8: Generalization between datasets, Llama-3-8b."
Context: Figures 7, 8 and 9 present the generalization results for the remaining models. While these results exhibit similar high-level patterns to those found in the main paper on Mistral-7b-instruct, notable differences suggest that these models may possess different mechanisms for encoding truthfulness.
Relevance: Figure 8 is relevant as it provides insights into the generalization capabilities of the Llama-3-8b model, complementing the analysis of Mistral-7b in Figure 7. Comparing the two figures allows for an assessment of whether the observed generalization patterns are model-specific or hold across different LLM architectures. This is crucial for understanding the broader applicability of the findings.
Figure 7 presents two heatmaps illustrating the generalization performance of Mistral-7b across different datasets. Heatmap (a) shows raw AUC values, where values above 0.5 suggest some level of generalization. Heatmap (b) shows the difference in AUC between the probe method and a logit-based method. Positive values indicate that the probe generalizes better than the logit-based method. The datasets used for both training and testing include TriviaQA, HotpotQA, Movies, Winobias, Winogrande, NLI, IMDB, Math, HotpotQA with context (WC), and Natural Questions with context (WC).
Text: "Figure 7: Generalization between datasets, Mistral-7b."
Context: C FULL GENERALIZATION RESULTS Figures 7, 8 and 9 present the generalization results for the remaining models. While these results exhibit similar high-level patterns to those found in the main paper on Mistral-7b-instruct, notable differences suggest that these models may possess different mechanisms for encoding truthfulness.
Relevance: Figure 7 provides a visual representation of the generalization capabilities of the Mistral-7b model. It helps to understand how well the model can transfer knowledge about truthfulness detection from one dataset to another. This is important for assessing the model's robustness and its potential for real-world applications where it might encounter unseen data.
Figure 8, similar to Figure 7, presents the generalization performance but for the Llama-3-8b model. It includes two heatmaps: (a) raw AUC values, where values above 0.5 suggest some generalization, and (b) the difference in AUC between the probe method and a logit-based method. Positive values in (b) indicate that the probe generalizes better than the logit-based method. The same datasets are used as in Figure 7.
Text: "Figure 8: Generalization between datasets, Llama-3-8b."
Context: C FULL GENERALIZATION RESULTS Figures 7, 8 and 9 present the generalization results for the remaining models. While these results exhibit similar high-level patterns to those found in the main paper on Mistral-7b-instruct, notable differences suggest that these models may possess different mechanisms for encoding truthfulness.
Relevance: Figure 8 is important because it allows for a comparison of the generalization performance between Mistral-7b (shown in Figure 7) and Llama-3-8b. This comparison helps to understand whether the observed generalization patterns are model-specific or more general across different LLM architectures.
Figure 9 presents the generalization performance of Llama-3-8b-instruct across different datasets, using the same format as Figures 7 and 8. It includes two heatmaps: (a) raw AUC values, where values above 0.5 suggest some generalization, and (b) the difference in AUC between the probe method and a logit-based method. Positive values in (b) indicate better generalization by the probe compared to the logit-based method.
Text: "Figure 9: Generalization between datasets, Llama-3-8b-instruct."
Context: C FULL GENERALIZATION RESULTS Figures 7, 8 and 9 present the generalization results for the remaining models. While these results exhibit similar high-level patterns to those found in the main paper on Mistral-7b-instruct, notable differences suggest that these models may possess different mechanisms for encoding truthfulness.
Relevance: Figure 9 completes the generalization analysis by showing the results for the instruction-tuned version of the Llama model. This allows for a comparison between the base Llama model (Figure 8) and its instructed counterpart, as well as with the Mistral models (Figures 3 and 7). This comparison helps to understand the impact of instruction tuning on generalization performance and whether the observed patterns are consistent across different model variants.
This appendix explains the way errors are categorized (taxonomy) in the research, providing more detail and justification for the chosen categories.
Including concrete examples of how errors are classified according to the taxonomy would significantly improve understanding.
Rationale: Examples would make the abstract categories more concrete and demonstrate the practical application of the taxonomy.
Implementation: Add a table or list of examples showing different error types and how they are classified based on the repeated sampling analysis.
A visual representation of the taxonomy, such as a flowchart or diagram, would enhance clarity and make it easier to grasp the different categories and their relationships.
Rationale: A visual representation would complement the textual description and provide a more intuitive understanding of the taxonomy.
Implementation: Create a flowchart or diagram illustrating the different error categories and the criteria used for classification.
Comparing the proposed taxonomy with existing error taxonomies in the literature would provide context and highlight its unique contributions.
Rationale: Comparing with other taxonomies would demonstrate the novelty and relevance of the proposed approach and position it within the broader field of LLM error analysis.
Implementation: Add a section discussing existing error taxonomies and comparing them with the proposed taxonomy, highlighting similarities and differences.
Figure 10 is a line graph showing the percentage of answers for which at least one generated answer was correct when resampling the LLM's response multiple times. The x-axis represents the number of resamples (from 1, which is equivalent to greedy decoding, up to 31). The y-axis represents the percentage of correct answers. The graph shows an increasing trend, meaning that as the number of resamples increases, the chance of getting at least one correct answer also increases. However, the rate of increase diminishes as the number of resamples grows, suggesting a point of diminishing returns.
Text: "Figure 10: The percentage of answers for which at least one generated answer was correct. The first step is greedy decoding."
Context: D TAXONOMY OF ERRORS Figure 10 presents, for each amount of resamples, the amount percentage of answers for which at least one generated answer was correct. The experiment was done on Mistral-7b-instruct with the TriviaQA dataset. For many answers that the greedy decoding fails to correctly provide an answer, the LLM is still able to generate the correct answer in at least one resample. The plot plateues around 30 resamples.
Relevance: This figure is relevant because it justifies the choice of K=30 resamples used in the error type taxonomy. It shows that increasing the number of resamples improves the chances of finding the correct answer, but the improvement plateaus around 30, suggesting that further resampling would yield diminishing returns.
This appendix provides complete results for the experiments on detecting the correct answer within a pool of generated responses, expanding on the analysis in the main paper. It includes examples where the model internally encoded the correct answer but consistently generated an incorrect one. The appendix also presents tables comparing different answer selection strategies, including probe-based selection, for both instruct and non-instruct LLMs across various datasets. The results consistently show that probe-based selection improves accuracy, especially when the LLM doesn't have a strong preference for the correct answer during generation.
The appendix provides a complete set of results, allowing for a more thorough understanding of the answer selection experiments and supporting the findings in the main paper.
The appendix combines qualitative examples (Table 8) with quantitative results (Tables 9 and 10), providing a more holistic view of the probe's effectiveness in answer selection.
The appendix doesn't provide details on how the probe used for answer selection was trained. Providing more information about the probe's training data and objective would enhance reproducibility.
Rationale: Understanding the probe's training process is crucial for interpreting its behavior and replicating the experiments.
Implementation: Include details on the dataset used to train the probe, the training objective (e.g., error detection, answer correctness prediction), and any relevant hyperparameters.
While the appendix shows that the probe improves accuracy, it doesn't analyze how the probe selects the correct answer. Investigating the probe's behavior (e.g., which features it relies on) would provide deeper insights.
Rationale: Analyzing the probe's selection behavior could reveal the underlying mechanisms that contribute to its effectiveness and provide a better understanding of the LLM's internal representations.
Implementation: Analyze the probe's activations, attention patterns, or other relevant features to understand its answer selection strategy. For example, visualize the probe's attention on different parts of the LLM's output to see which tokens or features it focuses on when selecting an answer.
The appendix could discuss the implications of the findings for improving existing LLM decoding strategies or developing new ones. For example, how could the probe be integrated into the decoding process to improve answer selection?
Rationale: Connecting the findings to practical applications in LLM decoding would increase the impact of the research and provide directions for future work.
Implementation: Discuss potential ways to incorporate the probe into the decoding process, such as using the probe's probability scores to rerank or filter generated answers. Explore the potential benefits and challenges of such integration.
Table 8 presents examples where Mistral-7b-Instruct consistently generated the wrong answer but occasionally produced the correct one. The probe successfully identified the correct answer in these instances. The table shows five questions from TriviaQA, the incorrect answer most frequently generated, how many times that incorrect answer was generated out of 30 samples, the correct answer, and how many times the correct answer was generated. This table highlights the disconnect between the model's internal knowledge and its external behavior.
Text: "Table 8: Examples of questions where Mistral-7b-Instruct consistently provided incorrect answers but occasionally generated the correct one. In these instances, the probe successfully identified the right answer. For each question, the model was samples 30 times."
Context: In Table 8 we present some qualitative samples from Mistral-7b-instruct, for the phenomenon we observe at error type (C2) Consistently incorrect but generates the correct answer at least one time. The samples in the table represent cases where the probe chose the correct answer. Table 9 compares different decoding mechanisms, including the choice via probe, on non-instruct models, and Table 10 compares on the instruct models. For all datasets and models, we observe similar conclusions to those in the main paper: significant improvement is observed for error types where the LLM shows no preference to the correct answer.
Relevance: Table 8 is highly relevant because it provides specific examples demonstrating that the LLM sometimes knows the correct answer but fails to generate it consistently. This supports the paper's key finding of a discrepancy between the LLM's internal knowledge and its external behavior. The probe's ability to identify the correct answer from the sample pool further emphasizes its effectiveness.
Table 9 compares different answer choice strategies for non-instruct LLMs (Mistral-7b and Llama-8b) across three datasets (TriviaQA, Math, Winobias). The strategies include greedy decoding, random sampling, majority vote (choosing the most frequent answer), and using the probe. The table shows the performance (accuracy with standard deviation) of each strategy for different error types, as defined in the paper's taxonomy. Hyphens indicate missing data or inapplicable strategies. The table highlights how different strategies perform for various error types and models, providing a comprehensive comparison.
Text: "Table 9: Various answer choice strategies, non-instruct models."
Context: In Table 8 we present some qualitative samples from Mistral-7b-instruct, for the phenomenon we observe at error type (C2) Consistently incorrect but generates the correct answer at least one time. The samples in the table represent cases where the probe chose the correct answer. Table 9 compares different decoding mechanisms, including the choice via probe, on non-instruct models, and Table 10 compares on the instruct models. For all datasets and models, we observe similar conclusions to those in the main paper: significant improvement is observed for error types where the LLM shows no preference to the correct answer.
Relevance: Table 9 is relevant as it provides a detailed comparison of different answer selection strategies for non-instruct LLMs. It shows how these strategies perform across various error types and datasets, offering insights into their strengths and weaknesses. This comparison helps to understand the effectiveness of the probe-based selection method relative to other strategies.
Table 10 presents the accuracy of different answer selection strategies for instruction-tuned language models on the TriviaQA, Math, and Winobias datasets. The strategies include greedy decoding, random sampling, majority voting (choosing the most frequent answer), and probe-based selection. The results are broken down by error type, which categorizes the model's response patterns across multiple generations of the same question. The table shows how the accuracy of each strategy varies depending on the type of error the model makes.
Text: "Table 10: Various answer choice strategies, instruct models."
Context: In Table 8 we present some qualitative samples from Mistral-7b-instruct, for the phenomenon we observe at error type (C2) Consistently incorrect but generates the correct answer at least one time. The samples in the table represent cases where the probe chose the correct answer. Table 9 compares different decoding mechanisms, including the choice via probe, on non-instruct models, and Table 10 compares on the instruct models. For all datasets and models, we observe similar conclusions to those in the main paper: significant improvement is observed for error types where the LLM shows no preference to the correct answer.
Relevance: Table 10 is relevant because it directly compares the performance of different answer selection strategies, including the proposed probe-based method, for instruction-tuned LLMs. It shows how the effectiveness of each strategy varies depending on the type of error the LLM makes, providing insights into the strengths and weaknesses of each approach.