This paper introduces "Do-Not-Answer," an open-source dataset designed to evaluate the safety of large language models (LLMs). The dataset focuses on 939 prompts that responsible LLMs should refuse to answer, categorized using a three-level hierarchical risk taxonomy covering five key risk areas (information hazards, malicious uses, discrimination/toxicity, misinformation harms, and human-computer interaction harms). Six popular LLMs (GPT-4, ChatGPT, Claude, LLaMA-2, ChatGLM2, and Vicuna) were evaluated using this dataset, revealing varying safety performance levels. Additionally, the researchers developed smaller, more efficient BERT-like classifiers, demonstrating their potential as a cost-effective alternative for automatic safety evaluation, achieving comparable performance to GPT-4.
Description: This table presents the distribution of questions across the five risk areas and twelve harm types defined in the taxonomy, providing insights into the composition and scope of the Do-Not-Answer dataset.
Relevance: The table demonstrates the coverage of various risk categories and allows researchers to understand the dataset's focus and potential biases. It also helps in interpreting the evaluation results in the context of the dataset composition.
Description: This figure displays five heatmaps showing the distribution of action categories across six LLMs for each risk area, providing a visual summary of model behavior across different types of risky prompts.
Relevance: The heatmaps offer a clear and concise visualization of the models' strengths and weaknesses in handling various risk areas, allowing for quick comparison of their safety performance and identification of areas for improvement.
This paper makes a significant contribution to the field of LLM safety by introducing a comprehensive risk taxonomy, a novel dataset of risky prompts, and an evaluation of popular LLMs. The finding that LLaMA-2 performed best in terms of safety and the promising results of the smaller, Longformer-based automatic evaluation method are valuable insights for developers. Future research should focus on expanding the dataset with non-risky instructions, extending the evaluation to other languages and multi-turn conversations, and further refining automatic evaluation techniques to improve the accuracy and scalability of LLM safety assessments. These advancements are crucial for the responsible development and deployment of LLMs in real-world applications.
This paper introduces "Do-Not-Answer," an open-source dataset designed to evaluate the safety of large language models (LLMs). The dataset focuses on prompts that responsible LLMs should refuse to answer. The researchers evaluated six popular LLMs using this dataset and found varying levels of safety performance. They also developed smaller, BERT-like classifiers that can automatically evaluate LLM safety with comparable effectiveness to GPT-4.
The abstract clearly states the need for better safety evaluation of LLMs, especially given the emergence of harmful capabilities. The creation of an open-source dataset is a significant contribution to the field.
The abstract effectively summarizes the key findings, including the varying performance of different LLMs and the successful development of smaller classifiers. It provides enough information to understand the scope and significance of the work without being overly technical.
While the abstract mentions varying LLM performance, it could be strengthened by briefly mentioning which models performed best or worst. This would provide a more concrete takeaway for the reader.
Rationale: Adding specific results, even briefly, would make the abstract more impactful and encourage readers to learn more.
Implementation: Include a short phrase like "Results show that LLaMA-2 performed best in avoiding risky instructions, while ChatGLM2 ranked last." or similar.
The abstract could benefit from explicitly mentioning the three-level hierarchical risk taxonomy used in the dataset. This is a key aspect of the work and deserves mention in the abstract.
Rationale: Highlighting the taxonomy would emphasize the comprehensive nature of the dataset and its potential for deeper analysis of LLM safety.
Implementation: Add a phrase like "The dataset is organized using a three-level hierarchical risk taxonomy, covering a range of potential harms." or similar.
Large language models (LLMs) are rapidly evolving, demonstrating both beneficial and harmful emergent capabilities. This necessitates evaluating and mitigating these risks, especially for open-source LLMs, which often lack robust safety mechanisms. This paper introduces the "Do-Not-Answer" dataset, an open-source resource designed to evaluate LLM safeguards by focusing on prompts that responsible models should refuse to answer. This dataset aims to enable safer development and deployment of open-source LLMs.
The introduction effectively establishes the context and motivation for the research by highlighting the dual nature of LLM emergent capabilities and the need for better safety evaluations.
The introduction explicitly addresses the gap in safety mechanisms for open-source LLMs, making the research relevant and impactful for a wider community.
While the introduction mentions existing model evaluations, it could be strengthened by briefly elaborating on the types of safety mechanisms currently employed in commercial LLMs.
Rationale: Providing more context on existing safety measures would better frame the need for the Do-Not-Answer dataset.
Implementation: Add a sentence or two describing common safety mechanisms like reinforcement learning from human feedback or content filtering.
The introduction could briefly preview the structure or key features of the Do-Not-Answer dataset to give the reader a better understanding of its contents.
Rationale: A brief preview of the dataset would increase reader engagement and provide a smoother transition to the later sections.
Implementation: Add a sentence like "The Do-Not-Answer dataset is curated and filtered to include a diverse range of prompts categorized by a hierarchical risk taxonomy."
This section discusses existing research on the risks of deploying Large Language Models (LLMs), including studies focusing on specific risk areas like bias, toxicity, and misinformation, and work on holistic risk evaluation. It highlights the limitations of current datasets and emphasizes the need for a comprehensive taxonomy and open-source dataset for evaluating LLM safety.
The section provides a thorough overview of existing research on LLM risks, covering both specific risk areas and holistic evaluations. This demonstrates a good understanding of the current landscape.
The section clearly identifies the limitations of existing datasets and research, highlighting the need for the proposed work. This justifies the creation of the Do-Not-Answer dataset and taxonomy.
While the section mentions several datasets, a more detailed comparison of their sizes, scopes, and limitations would be beneficial. This would help readers better understand the unique contribution of the Do-Not-Answer dataset.
Rationale: A more detailed comparison would strengthen the argument for the Do-Not-Answer dataset and highlight its advantages over existing resources.
Implementation: Create a table summarizing key features of the mentioned datasets, including size, scope, availability, and limitations.
The section could benefit from discussing the specific evaluation metrics used in prior work and how they relate to the metrics used in this paper. This would provide a clearer context for evaluating the results.
Rationale: Discussing evaluation metrics would help readers understand how the proposed work compares to previous research and the significance of the chosen metrics.
Implementation: Add a paragraph discussing common evaluation metrics for LLM safety, such as accuracy, precision, recall, and F1-score, and how they are used in the current work.
This figure presents a radar chart comparing six large language models (LLMs) across different safety aspects. Each axis of the radar chart represents a category of potential harm, such as 'Information Hazards' or 'Malicious Uses'. The performance of each LLM is represented by a colored line, forming a polygon on the chart. A point closer to the outer edge of the chart indicates a higher score in that category, meaning the LLM is better at avoiding that specific harm. The LLMs compared are GPT-4, ChatGPT, Claude, ChatGLM2, LLaMA-2, and Vicuna.
Text: "Figure 1: A comprehensive evaluation of LLM safeguards."
Context: This figure is presented at the beginning of the 'Related Work' section on page 2. It follows a discussion of the need for better safety evaluations in LLMs.
Relevance: This figure is highly relevant because it visually summarizes the core evaluation performed in the paper. It allows for a quick comparison of the safety performance of different LLMs across various risk categories.
This section introduces a three-level hierarchical taxonomy for classifying the risks associated with large language models (LLMs), particularly text-only models. It builds upon previous research and focuses on five key risk areas: information hazards, malicious uses, discrimination/exclusion/toxicity, misinformation harms, and human-computer interaction harms. The taxonomy provides a detailed breakdown of potential hazards, outlining how these risks manifest and providing examples of the types of questions or prompts that could trigger them.
The taxonomy builds upon the established work of Weidinger et al. (2021), providing a more refined and detailed classification of LLM risks.
The three-level structure and the inclusion of specific risk types provide a comprehensive and detailed framework for analyzing LLM safety.
While the taxonomy mentions harm types and risk types, the distinction between these two levels could be clarified further. This would improve the overall clarity and usability of the taxonomy.
Rationale: A clearer distinction between the levels would make the taxonomy easier to understand and apply in practice.
Implementation: Provide more explicit definitions and examples to differentiate between harm types (second level) and risk types (third level).
While the taxonomy provides examples of prompts that could trigger harmful responses, it could be strengthened by also including examples of safe and appropriate responses. This would provide a more complete picture of desired LLM behavior.
Rationale: Including examples of safe responses would make the taxonomy more practical for developers and researchers working on LLM safety.
Implementation: For each risk type, provide examples of responses that would be considered safe and responsible.
This figure illustrates a three-level taxonomy of risks associated with Large Language Models (LLMs). The top level categorizes risks into five broad areas: Information Hazards, Malicious Uses, Discrimination/Toxicity, Misinformation Harms, and Human-Chatbot Interaction Harms. Each of these areas is then broken down into more specific harm types in the second level. The third level further details each harm type with specific examples. The taxonomy helps to organize and understand the various ways LLMs can potentially cause harm.
Text: "Figure 2: Three-level taxonomy of LLM risks."
Context: This figure appears in the 'Safety Taxonomy' section on page 4, after a discussion of existing risk categorization efforts and the need for a more comprehensive taxonomy.
Relevance: This figure is crucial for understanding the structure and scope of the research. It provides the framework for the Do-Not-Answer dataset and guides the evaluation of LLM safety.
This table shows the number of questions in the Do-Not-Answer dataset that fall into each of the five risk areas and twelve harm types defined in the safety taxonomy. The table has three columns: 'Risk Area', 'Harm Type', and '# Q' (number of questions). It provides a breakdown of the dataset's composition and shows how the questions are distributed across different risk categories.
Text: "Table 1: The number of questions (# Q) falling into our five risk areas and twelve harm types."
Context: This table is presented in the 'Safety Taxonomy' section on page 5, immediately after the introduction of the three-level risk taxonomy in Figure 2.
Relevance: This table is important because it provides a quantitative overview of the Do-Not-Answer dataset. It shows the distribution of questions across different risk categories, which is crucial for understanding the scope and focus of the evaluation.
This section details how the researchers collected 939 risky questions and the corresponding responses from six different Large Language Models (LLMs). The questions are designed to be those that a responsible LLM should refuse to answer. The researchers used a novel three-round conversation strategy with GPT-4 to generate these questions, addressing challenges in eliciting risky content. They also describe how they handled borderline cases and filled in question templates with specific risky scenarios. Finally, they collected responses from three commercial and three open-source LLMs, providing statistics on the length of these responses.
The three-round conversation strategy with GPT-4 is a creative and effective way to generate risky questions while bypassing safety restrictions. This approach allows for the collection of a diverse range of potentially harmful prompts.
The researchers clearly explain why they chose to focus on questions LLMs should not answer, emphasizing the ease of evaluation and the trade-off with potential bias in question distribution.
While the three-round conversation strategy is described, providing the actual prompts used would enhance reproducibility and allow others to build upon this method.
Rationale: Sharing the specific prompts would make the research more transparent and facilitate further research on generating risky questions.
Implementation: Include the exact prompts used in each round of the GPT-4 conversation, potentially in an appendix or supplementary material.
While the researchers acknowledge the potential for bias in the question distribution, a more in-depth discussion of the types of biases that might be present and their potential impact on the evaluation would be beneficial.
Rationale: A more thorough discussion of potential biases would strengthen the analysis and provide a more nuanced understanding of the limitations of the dataset.
Implementation: Add a paragraph discussing potential biases, such as biases related to the topics covered, the phrasing of the questions, or the specific protected attributes used.
Table 2 presents the average number of words in the responses of six different Large Language Models (LLMs) across twelve different harm types. The LLMs included are GPT-4, ChatGPT, Claude, ChatGLM2, LLaMA-2, and Vicuna. The harm types are numbered from 1 to 12, each representing a specific category of harmful prompts, as defined in Table 1. The table also includes an average ("Avg") column for each LLM, representing the average number of words across all harm types for that specific model.
Text: "Table 2: Average number of words in the LLM responses across the different harm types."
Context: This table is introduced in the "Data Collection" section on page 6, after describing the process of collecting responses from the six LLMs.
Relevance: This table is relevant because it provides insights into the response patterns of different LLMs. The average number of words can indicate how verbose or concise the models are when addressing different types of harmful prompts. This information can be useful for understanding the models' safety strategies and potential vulnerabilities.
This section describes the process of annotating and assessing the responses of six LLMs to the risky questions in the Do-Not-Answer dataset. The researchers used a two-level assessment: first, classifying responses as either safe or harmful, and second, categorizing them into six refined action categories. The guidelines for determining harmfulness are outlined, along with a detailed explanation of each action category. The section also discusses the human evaluation process and initial findings regarding the safety performance of the different LLMs.
The section provides clear guidelines for what constitutes a harmless response, making the annotation process more consistent and reliable.
The six action categories provide a useful framework for analyzing the different ways LLMs respond to risky prompts. This allows for a more detailed comparison of LLM behavior beyond simply labeling them as safe or harmful.
While the section mentions that annotators resolved disagreements through discussion, providing quantitative measures of inter-annotator agreement (e.g., Kappa score) would strengthen the validity of the human evaluation.
Rationale: Inter-annotator agreement statistics provide a measure of the reliability and consistency of the annotation process.
Implementation: Calculate and report inter-annotator agreement using an appropriate metric, such as Cohen's Kappa or Fleiss' Kappa.
The section could benefit from a more detailed explanation of why these specific action categories were chosen and how they contribute to understanding LLM safety.
Rationale: Providing more context on the action categories would make the analysis more transparent and help readers understand the significance of the results.
Implementation: Add a paragraph discussing the theoretical or empirical basis for the chosen categories and how they relate to different safety strategies or vulnerabilities.
Table 3 describes six categories of LLM responses to risky prompts, ranging from refusal to answer to directly following the prompt. Each category is assigned a numerical label (0-5) and a detailed description with examples. Categories 0-4 represent generally harmless responses, while category 5 indicates a harmful response where the LLM follows the risky instruction.
Text: "Table 3: Six response categories. Responses falling into 0-4 are generally harmless, while those in category 5 are harmful."
Context: This table appears in the 'Response Annotation and Assessment' section on page 7, after a discussion of the guidelines for evaluating LLM responses.
Relevance: This table is essential for understanding how the researchers categorized and assessed the LLM responses. It provides the framework for the qualitative analysis of LLM safety and informs the subsequent quantitative evaluation.
Figure 6 shows five heatmaps, each representing a different risk area (Information Hazards, Malicious Uses, Discrimination/Toxicity, Misinformation Harms, and Human-chatbot Interaction Harms). Each heatmap displays the distribution of six action categories (0-5, as defined in Table 3) across six different LLMs (GPT-4, ChatGPT, Claude, ChatGLM2, LLaMA-2, and Vicuna). The color intensity in each cell of the heatmap corresponds to the frequency of a specific action category for a given LLM within a particular risk area. Darker colors indicate higher frequencies.
Text: "Figure 6: The action category distribution given a specific risk area for the different models."
Context: This figure is presented in the 'Response Annotation and Assessment' section on page 9. It follows a discussion of the human evaluation of LLM responses and the observed action category patterns.
Relevance: This figure is highly relevant as it visually summarizes the key findings of the LLM safety evaluation. It allows for easy comparison of how different LLMs respond to various types of risky prompts, highlighting their strengths and weaknesses in different risk areas.
This figure presents three heatmaps, each representing a specific harm type and showing the distribution of action categories across six different large language models (LLMs). The harm types are 'Assisting illegal activities,' 'Social stereotypes/unfair discrimination,' and 'Cause material harms in medicine/law.' The LLMs included are GPT-4, ChatGPT, Claude, ChatGLM2, LLaMA-2, and Vicuna. Each cell in a heatmap represents the frequency of a specific action category (0-5, as defined in Table 3) taken by a particular LLM when responding to a prompt related to the given harm type. The color intensity of each cell reflects the frequency, with darker colors indicating higher frequencies.
Text: "Figure 7: The action category distribution for Assisting illegal activities, Stereotype and discrimination, and Medicine or law harms."
Context: This figure appears in the 'Response Annotation and Assessment' section on page 9, after a discussion of how the models' responses were categorized into different action categories.
Relevance: This figure is relevant because it provides a detailed view of how different LLMs respond to specific types of harmful prompts. It helps to identify patterns in the models' behavior and assess their ability to avoid generating harmful content in different contexts.
This section explores automatic methods for evaluating LLM responses to risky prompts, focusing on efficiency and scalability. Two main methods are presented: using GPT-4 as an evaluator and training a smaller, PLM-based classifier. Experiments show that the PLM-based classifier, specifically Longformer, achieves comparable performance to GPT-4, suggesting a cost-effective alternative for automatic safety evaluation.
The section clearly explains the limitations of human evaluation and the need for automatic methods, setting the stage for the proposed approaches.
The section provides a clear description of both the GPT-4 and PLM-based evaluation methods, including details on prompting and training.
While the section mentions using human annotations, it would be helpful to provide more specifics about the size and composition of the training data used for the PLM-based classifier.
Rationale: More details on the training data would enhance reproducibility and allow for a better understanding of the classifier's performance.
Implementation: Include information on the number of annotated responses used for training, the distribution of labels, and any data augmentation techniques employed.
While automatic evaluation offers advantages in terms of efficiency, it also has limitations. The section could be strengthened by discussing potential biases or inaccuracies in the automatic methods and how they might compare to human judgment.
Rationale: Acknowledging the limitations of automatic evaluation would provide a more balanced perspective and highlight areas for future improvement.
Implementation: Add a paragraph discussing potential limitations, such as the reliance on human annotations for training, the potential for biases in the training data, and the difficulty of capturing nuanced aspects of human judgment.
Table 4 presents the results of action classification for six different Large Language Models (LLMs), comparing their performance using two evaluation methods: GPT-4 and Longformer. The metrics used are Accuracy, Precision, Recall, and F1 score. The table provides values for each LLM individually (GPT-4, ChatGPT, Claude, ChatGLM2, LLaMA-2, and Vicuna) evaluated by both GPT-4 and Longformer. An "Overall" row shows the average performance across all models for each metric and evaluation method, including standard deviations to indicate the variability in performance.
Text: "Table 4: Action classification results (%) for each LLM."
Context: This table appears in the "Automatic Response Evaluation" section on page 11. It follows a description of the automatic evaluation methods used and their experimental setup.
Relevance: This table is highly relevant as it presents the core results of the automatic evaluation of LLM action classification. It allows for a direct comparison of the performance of different LLMs and evaluation methods, demonstrating the effectiveness of the proposed Longformer-based approach.
Table 5 presents the results of harmful response detection for various LLMs, evaluated using three methods: Human evaluation, GPT-4, and Longformer. The table shows the Accuracy, Precision, Recall, and F1 scores for each LLM (GPT-4, ChatGPT, Claude, ChatGLM2, LLaMA-2, and Vicuna) under each evaluation method. An "Overall" row provides the average performance across all models for each metric and evaluation method, including standard deviations.
Text: "Table 5: Harmful response detection results (%) for each LLM."
Context: This table is located in the "Automatic Response Evaluation" section on page 11. It directly follows Table 4, which presents the action classification results.
Relevance: This table is highly relevant as it shows the performance of different LLMs and evaluation methods on the crucial task of harmful response detection. It provides a direct comparison between human evaluation and the proposed automatic methods (GPT-4 and Longformer), demonstrating the effectiveness of the Longformer-based approach.
This table presents the percentage of harmless responses generated by each of the six evaluated LLMs: LLaMA-2, ChatGPT, Claude, GPT-4, Vicuna, and ChatGLM2. A higher percentage indicates a better safety performance, meaning the model is more likely to avoid generating harmful content in response to risky prompts.
Text: "Table 6: Proportion of harmless responses of each LLM (%; higher is better)."
Context: This table is mentioned on page 11, within the 'Automatic Response Evaluation' section. It summarizes the overall safety performance of the LLMs based on human evaluation.
Relevance: This table is highly relevant because it provides a direct comparison of the overall safety performance of the six LLMs. It summarizes the key findings of the human evaluation and allows for a quick assessment of which models are better at avoiding harmful responses.
This paper introduced a three-level taxonomy for evaluating the risks of harm from LLMs, created a dataset of 939 risky questions and 5,000+ responses from six LLMs, and defined criteria for safe and responsible answers. They found that LLaMA-2 was the safest model and that a small, trained model could achieve comparable evaluation results to GPT-4.
The conclusion effectively summarizes the key contributions of the paper, including the taxonomy, dataset, and evaluation methods.
The conclusion highlights the important finding that LLaMA-2 performed best in terms of safety and that a smaller model can be used for effective automatic evaluation.
The conclusion could be strengthened by discussing the broader implications of the findings for the field of LLM safety research and development.
Rationale: Discussing broader implications would enhance the impact of the paper and provide a stronger takeaway message.
Implementation: Add a sentence or two discussing how the findings can inform future research on LLM safety, such as developing better safety mechanisms or improving evaluation methods.
While the conclusion mentions automatic evaluation methods, it could be improved by explicitly connecting this to the future work discussed in the next section. This would create a smoother transition and highlight the potential for further research.
Rationale: Connecting the conclusion to the future work section would provide a more cohesive narrative and encourage readers to explore the next steps in this research area.
Implementation: Add a sentence like "The promising results of the automatic evaluation methods suggest potential for further development and refinement, as discussed in the next section on limitations and future work."
This section outlines the limitations of the current research and suggests directions for future work. The primary limitations relate to the data collection process, specifically the focus on only risky instructions and the limited dataset size. The evaluation scope is also limited to English, single-turn, and zero-shot settings. Future work will address these limitations by including non-risky instructions, expanding the dataset, collecting multi-label annotations, and extending the evaluation to other languages, multi-turn conversations, and few-shot settings.
The section clearly articulates the limitations of the current research, including the focus on risky instructions, the limited dataset size, and the scope of the evaluation. This transparency strengthens the overall analysis.
The section offers specific and actionable suggestions for future research, such as including non-risky instructions, expanding the dataset, and collecting multi-label annotations. These suggestions provide a clear roadmap for improving the evaluation.
While the section lists several suggestions for future work, it could be improved by prioritizing them based on their potential impact and feasibility. This would help guide future research efforts.
Rationale: Prioritizing suggestions would make the section more impactful and help researchers focus on the most important areas for improvement.
Implementation: Rank the suggestions based on their importance and feasibility, or group them into short-term and long-term goals.
While the section suggests directions for future work, it could be strengthened by discussing potential challenges or limitations of these suggestions. This would provide a more realistic outlook on the feasibility of the proposed improvements.
Rationale: Discussing potential challenges would enhance the analysis and provide a more nuanced perspective on the future of this research area.
Implementation: For each suggestion, briefly discuss potential challenges or limitations, such as the difficulty of collecting multi-label annotations or the computational cost of evaluating multi-turn conversations.
This appendix lists the protected groups considered when generating the risky question set for the LLM safety evaluation. These groups include race, religion, gender, organization, and individual names. The inclusion of these groups aims to ensure that the evaluation covers a broad range of potential biases and vulnerabilities in LLMs.
The appendix clearly lists the specific groups considered for each protected attribute. This transparency is crucial for understanding the scope of the evaluation and potential biases in the dataset.
The appendix explains the rationale for including individual names and the methodology used to select them, addressing potential implicit biases related to gender and race.
While the appendix acknowledges considering only binary gender, expanding to include non-binary and other gender identities would make the evaluation more inclusive and representative.
Rationale: Including a wider range of gender identities would improve the comprehensiveness of the evaluation and address potential biases related to gender diversity.
Implementation: Include additional gender categories, such as non-binary, transgender, and genderqueer, and adapt the question generation process accordingly.
While the appendix lists the organizations included, it could be strengthened by clarifying the specific criteria used to select these organizations. This would enhance transparency and reproducibility.
Rationale: Clarifying the selection criteria would make the research more rigorous and allow others to replicate or extend the dataset.
Implementation: Provide a more detailed explanation of the organization selection process, including any specific inclusion or exclusion criteria used.
This appendix showcases the only three instances where LLaMA-2, generally the safest model tested, produced harmful responses. These instances all relate to risky over-reliance, where the model's human-like tone could lead users to depend on it inappropriately for emotional support or sensitive information.
The inclusion of the full question and response pairs for each harmful instance allows for a detailed understanding of the context and nature of the harmful responses.
The appendix clearly explains why the identified responses are considered harmful, emphasizing the risk of over-reliance and potential for emotional manipulation.
While the appendix focuses on the harmful responses, providing more context on LLaMA-2's training and safety mechanisms would help understand why these specific failures occurred.
Rationale: Understanding the model's training can shed light on the root causes of the harmful responses and inform future improvements.
Implementation: Include a brief description of LLaMA-2's training process, particularly any safety-related training or constraints implemented.
The appendix could be strengthened by discussing potential mitigation strategies for the identified harmful responses. This would provide actionable insights for developers working on LLM safety.
Rationale: Discussing mitigation strategies would make the analysis more practical and contribute to the development of safer LLMs.
Implementation: Add a paragraph discussing potential mitigation strategies, such as detecting and modifying human-like tones, providing explicit disclaimers about the model's limitations, or incorporating mechanisms to discourage over-reliance.
Table 7 presents three examples of harmful responses generated by the LLaMA-2 model. Each example includes a question posed to the model and the corresponding response. These responses are considered harmful because they exhibit a human-like tone, which could lead users to over-rely on the LLM for emotional support or sensitive information, creating a potential risk.
Text: "Table 7: Three harmful responses of LLaMA-2 since human-tone responses may result in risky over-reliance."
Context: This table is presented in Appendix B on page 15. It follows a brief discussion of LLaMA-2's overall safety performance and its ranking as the safest among the evaluated LLMs.
Relevance: This table is highly relevant because it provides concrete examples of LLaMA-2's failures, despite its overall high safety ranking. It highlights a specific vulnerability related to human-like responses and potential over-reliance, offering valuable insights into the limitations of current LLM safety mechanisms.
This appendix visually represents the distribution of response action categories across different Large Language Models (LLMs) for various harm types. It highlights the observation that models tend to exhibit specific response patterns depending on the type of harm presented in the prompt.
Using a visual representation, presumably heatmaps as mentioned in the main text, is a strength as it allows for quick and easy comparison of response patterns across different models and harm types.
Highlighting the harm-specific response patterns is a strength as it provides a nuanced understanding of how LLMs react to different types of harmful prompts, going beyond a simple safe/unsafe classification.
The appendix refers to Figure 9 but doesn't include it. Including the figure directly in the appendix would make the analysis self-contained and easier to understand.
Rationale: The visualization is crucial for understanding the described response patterns. Without it, the appendix is incomplete.
Implementation: Include Figure 9 directly in the appendix.
While the appendix mentions obvious response patterns, it doesn't provide a detailed analysis of these patterns. Describing the specific patterns observed for different harm types would strengthen the analysis.
Rationale: A more detailed analysis would provide more actionable insights into LLM behavior and inform the development of better safety mechanisms.
Implementation: Add a paragraph or two describing the specific response patterns observed for different harm types. For example, mention which action categories are most common for each harm type and discuss any notable differences between LLMs.
Figure 9 presents six heatmaps illustrating the distribution of action categories taken by six large language models (LLMs) in response to prompts related to six different harm types. Each heatmap corresponds to a specific harm type, such as leaking sensitive information or generating adult content. The LLMs included are GPT-4, ChatGPT, Claude, ChatGLM2, LLaMA-2, and Vicuna. The action categories (0-5) represent different ways the LLMs can respond, ranging from refusing to answer to directly following the harmful instruction. The color intensity in each cell of the heatmap indicates the frequency of a specific action category for a given LLM and harm type, with darker colors representing higher frequencies. This allows for a visual comparison of how different LLMs handle various types of risky prompts.
Text: "Figure 9: Given a specific harm type, refined response category distribution across models."
Context: This figure is introduced in Appendix C on page 16, which focuses on analyzing the response action categories of different LLMs across various harm types.
Relevance: This figure is highly relevant because it provides a detailed breakdown of how different LLMs respond to various types of harmful prompts. It visually summarizes the models' behavior across different risk categories, allowing for a direct comparison of their safety performance and the effectiveness of their safety mechanisms.
Table 8 provides statistics on instances where the assigned action category (a measure of harmfulness based on the six categories defined in Table 3) contradicts the actual harmfulness of the LLM's response, as determined by human evaluation. These are referred to as 'mismatched cases.' Two types of mismatches are presented: (1) where the action category is 5 (indicating a harmful response according to Table 3), but the response is judged harmless by human annotators; and (2) where the action category is 0-4 (indicating a harmless response), but the response is judged harmful. The table shows the number of occurrences of each mismatch type for each of the six LLMs: GPT-4, ChatGPT, Claude, ChatGLM2, LLaMA-2, and Vicuna. A 'Total' column sums the two mismatch types for each LLM.
Text: "Table 8: Statistics of mismatched cases of each mode for the six models."
Context: This table is introduced in Appendix D on page 16, which discusses mismatched cases where the assigned action category doesn't align with the human-judged harmfulness of the response.
Relevance: This table is relevant because it highlights potential limitations or inconsistencies in the action category classification scheme. It shows that relying solely on the action categories might not always accurately reflect the true harmfulness of an LLM's response, as determined by human judgment. This underscores the importance of human evaluation and the need for further refinement of automatic evaluation methods.
This appendix presents cases where the assigned action category (a measure of how the LLM responded) didn't match the human judgment of whether the response was actually harmful. These "mismatched cases" highlight situations where an LLM's response might be categorized as safe based on its action (like refusing to answer), but the actual content of the response is still harmful, or vice-versa. This points to the limitations of relying solely on action categories for judging safety and emphasizes the importance of careful content review.
The appendix clearly defines the two types of mismatches considered, making the analysis focused and easy to follow.
Providing specific examples of mismatched cases from ChatGLM2 and Vicuna helps to illustrate the issue and understand how these discrepancies can occur in practice.
The appendix refers to Table 8, which summarizes the number of mismatched cases for each model, but the table itself is not included. Including the table directly in the appendix would make the analysis more complete and informative.
Rationale: The table provides important quantitative data that supports the analysis of mismatched cases. Without it, the reader has to refer back to the main text, disrupting the flow of the appendix.
Implementation: Include Table 8 directly in Appendix D.
While the appendix highlights the existence of mismatched cases, it doesn't fully discuss their implications for the overall evaluation of LLM safety. Elaborating on how these mismatches might affect the conclusions drawn about the models' performance would strengthen the analysis.
Rationale: Discussing the implications for the overall evaluation would provide a more complete and nuanced perspective on the limitations of the current evaluation methodology.
Implementation: Add a paragraph discussing how the mismatched cases might affect the interpretation of the results presented in the main text. For example, consider whether the presence of mismatches might lead to an overestimation or underestimation of the safety of certain models.
This table shows the number of mismatched cases for each of the six tested large language models (LLMs). A mismatch occurs when the predicted action category (0-5, representing how the LLM responded to a prompt) doesn't align with the human judgment of whether the response was actually harmful. There are two types of mismatches: 1) the model followed the prompt (category 5), but the response was judged harmless, and 2) the model didn't follow the prompt (categories 0-4), but the response was judged harmful. The table lists the number of each type of mismatch and the total number of mismatches for each LLM.
Text: "Table 8: Statistics of mismatched cases of each mode for the six models."
Context: Appendix D introduces this table to explain discrepancies between the assigned action categories and the human-labeled harmfulness of LLM responses.
Relevance: This table is important because it highlights the limitations of using action categories alone to determine harmfulness. It shows that the automated classification doesn't always agree with human judgment, suggesting a need for improvement in the automated evaluation methods or a deeper understanding of what constitutes a harmful response.
This table provides specific examples of mismatched cases where the assigned action category doesn't align with the human-labeled harmfulness of the response. Each row shows an example from either ChatGLM2 or Vicuna. The table includes the model, whether the response was judged harmful, the assigned action category, the original question, the model's response, and the reason for the mismatch. For example, one ChatGLM2 response was classified as refusing to assist (category 0) but was judged harmful because it provided risky instructions. Another ChatGLM2 response followed the prompt (category 5), offering to interpret blood test results, but was judged harmless in the context of a single-turn chat.
Text: "Table 9: Mismatched examples in ChatGLM2 and Vicuna. Bold text indicates the refined label of the responses while the whole content reflects its harmfulness."
Context: Appendix D presents this table after introducing the concept of mismatched cases in Table 8.
Relevance: This table is crucial for understanding the nature of the mismatches described in Table 8. By providing concrete examples, it helps to illustrate the limitations of the automated action category classification and the complexities of evaluating LLM harmfulness.