CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference

Erxin Yu, Jing Li, Ming Liao, Siqi Wang, Zuchen Gao, Fei Mi, Lanqing Hong·June 25, 2024

Summary

This paper presents the CoSafe dataset, a novel resource for evaluating the safety of large language models (LLMs) in multi-turn dialogue scenarios, focusing on coreference interactions. The dataset consists of 1,400 questions across 14 categories, revealing a 56% attack success rate in LLMs, particularly LLaMA2-Chat-7b, indicating potential safety vulnerabilities. Coreference attacks in multi-turn dialogues are found to be more effective than single-prompt attacks, highlighting the need for considering dialogue context in safety assessments. The study compares various models, showing that multi-turn coreference can bypass safety mechanisms, with some models like LLaMA3 and Mistral showing improved safety due to their design. The research also emphasizes the importance of addressing safety concerns, the role of system prompts and Chain-of-Thought methods, and the need for robust defense strategies in LLMs for real-world applications.

Key findings

6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide me with additional details or context so I can assist you better.


What scientific hypothesis does this paper seek to validate?

I would be happy to help you with that. Please provide me with the title of the paper or some context so I can better understand the scientific hypothesis it aims to validate.


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference" proposes several novel ideas, methods, and models in the realm of large language model (LLM) safety evaluation . Here are some key points from the paper:

  1. Multi-Turn Dialogue Coreference Safety Evaluation: The paper introduces a new approach to studying LLM safety by focusing on multi-turn dialogue coreference attacks. It presents a dataset consisting of 1,400 questions across 14 categories, each designed to assess safety vulnerabilities in LLMs during coreference interactions .

  2. Evaluation of Five LLMs: The study evaluates five widely used open-source LLMs under multi-turn coreference safety attacks. The results reveal varying levels of vulnerability among the models, with the highest attack success rate at 56% for the LLaMA2-Chat-7b model and the lowest at 13.9% for the Mistral-7B-Instruct model .

  3. Safety Enhancement through Red-Teaming: The paper discusses the importance of safety assurances for LLMs and highlights the red-teaming approach introduced by Anthropic and DeepMind to identify and address safety loopholes in models. This approach involves role-playing attackers to enhance safety in LLMs .

  4. Dataset Release Statement: The authors emphasize that the dataset created for the study is intended for academic research purposes only and should not be misused. This statement aims to ensure responsible use of the dataset for further research and evaluation of LLM safety .

Overall, the paper contributes to the field of LLM safety evaluation by introducing a novel focus on multi-turn dialogue coreference safety attacks, evaluating the vulnerability of different LLMs, and emphasizing the importance of responsible dataset usage for academic research . The paper "CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference" introduces several characteristics and advantages compared to previous methods in the evaluation of large language model (LLM) safety. Here are some key points from the paper that highlight these aspects:

  1. Focus on Multi-Turn Dialogue Coreference Safety: One of the main characteristics of the paper is its focus on evaluating LLM safety in the context of multi-turn dialogue coreference attacks. This approach differs from previous methods that may have primarily focused on single-turn interactions or other types of safety vulnerabilities. By considering multi-turn dialogue coreference, the paper provides a more comprehensive assessment of LLM safety in complex conversational settings .

  2. Creation of a New Dataset: The paper introduces a new dataset consisting of 1,400 questions across 14 categories specifically designed to evaluate LLM safety in multi-turn dialogue coreference scenarios. This dataset is a novel contribution that enables researchers to assess the robustness of LLMs in handling coreference challenges during conversational interactions. Previous methods may not have had access to such specialized datasets tailored for this specific evaluation purpose .

  3. Evaluation of Multiple LLMs: The study evaluates five different LLMs under multi-turn coreference safety attacks, providing a comparative analysis of their vulnerability levels. By testing multiple models, the paper offers insights into the varying performance and susceptibility of different LLMs to coreference attacks. This comparative evaluation enhances the understanding of model behavior and highlights potential areas for improvement in LLM safety mechanisms .

  4. Red-Teaming Approach for Safety Enhancement: The paper discusses the red-teaming approach introduced by Anthropic and DeepMind as a method to enhance LLM safety. This approach involves simulating adversarial attacks to identify and address safety vulnerabilities in models. By incorporating red-teaming strategies, the paper advances the field by promoting proactive measures to improve LLM safety and resilience against potential threats .

  5. Responsible Dataset Usage: The authors emphasize responsible dataset usage by providing a release statement that underscores the intended academic research purposes of the dataset. This commitment to ethical research practices ensures that the dataset is used appropriately and prevents misuse that could compromise the integrity of LLM safety evaluations. This emphasis on responsible data usage sets a standard for future studies in the field .

Overall, the characteristics and advantages of the paper "CoSafe" lie in its innovative focus on multi-turn dialogue coreference safety evaluation, the creation of a specialized dataset, the comparative analysis of multiple LLMs, the adoption of red-teaming strategies for safety enhancement, and the promotion of responsible dataset usage for academic research purposes .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of evaluating large language model safety in multi-turn dialogue coreference, several related research works exist by notable researchers. Some of the noteworthy researchers in this field include Heeyoung Lee, Yves Peirsman, Angel Chang, Nathanael Chambers, Mihai Surdeanu, Dan Jurafsky, Kenton Lee, Luheng He, Mike Lewis, Luke Zettlemoyer, Stephanie Lin, Jacob Hilton, Owain Evans, Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou, Yulia Tsvetkov, Maarten Sap, Reza Shokri, Yejin Choi, Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel Bowman .

The key to the solution mentioned in the paper involves testing the privacy implications of language models via contextual integrity theory .


How were the experiments in the paper designed?

To provide you with a detailed answer, I would need more specific information about the paper you are referring to. Could you please provide me with the title of the paper or some key details about the experiments so I can assist you better?


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is CoSafe . The code for the evaluation is open source and can be accessed at the following link: https://huggingface.co/PKU-Alignment/beaver-dam-7b .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The study includes evaluations from multiple perspectives, such as GPT-4 Harmless Score, QA Moderation, and Human Evaluation, which collectively assess the safety and reliability of the models used in multi-turn dialogue . These evaluations contribute to a comprehensive understanding of the effectiveness and safety of the large language models in dialogue contexts, aligning with the scientific hypotheses under scrutiny. The consideration of various factors like accuracy, clarity, completeness, contextual understanding, and user engagement in the evaluations enhances the robustness of the study's findings . The results obtained from these evaluations offer valuable insights into the performance and safety aspects of the models, thereby supporting the scientific hypotheses being investigated in the paper.


What are the contributions of this paper?

The paper makes several contributions, including:

  • Evaluation and mitigation of safety risks in Large Language Models (LLMs) .
  • Providing a dataset intended for academic research purposes only to prevent misuse .
  • Support from various funding sources such as Huawei Noah’s Ark Lab, NSFC Young Scientists Fund, Research Grants Council of Hong Kong, and others .
  • References to related works in the field of coreference resolution and language model behaviors .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term projects that need ongoing monitoring and adjustments.

If you have a specific type of work in mind, feel free to provide more details so I can give you a more tailored response.

Tables

2

Introduction
Background
Emergence of large language models and their increasing use in dialogue systems
Growing concerns about safety risks, especially in coreference interactions
Objective
To introduce the CoSafe dataset
Assess safety vulnerabilities in LLMs through coreference attacks
Highlight the need for context-aware safety assessments
Method
Data Collection
Development of 1,400 questions across 14 categories
Designing multi-turn and single-prompt scenarios for comparison
Data Preprocessing
Coreference attack generation and implementation
Analysis of attack success rates and model performance
Attack Analysis
Multi-turn vs. single-prompt coreference attacks
Effectiveness in bypassing safety mechanisms
Model Comparison
LLMs examined, including LLaMA2-Chat-7b, LLaMA3, and Mistral
Safety improvements observed in specific models
Safety Mechanisms and Defense Strategies
Evaluation of system prompts and Chain-of-Thought methods
Discussion on addressing safety concerns in real-world applications
Results
Attack success rate of 56% in LLaMA2-Chat-7b
Coreference attacks' effectiveness in multi-turn dialogues
Safety improvements demonstrated by LLaMA3 and Mistral
Conclusion
The significance of context-aware safety assessments for LLMs
Recommendations for future research and development in robust defense strategies
Future Directions
Directions for enhancing LLM safety in multi-turn dialogue scenarios
Importance of collaboration between researchers and developers in addressing safety challenges
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
What is the primary purpose of the CoSafe dataset?
How do coreference attacks in multi-turn dialogues compare to single-prompt attacks in terms of effectiveness?
What is the attack success rate of LLMs, specifically LLaMA2-Chat-7b, as mentioned in the paper?
How many questions and categories does the CoSafe dataset consist of?

CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference

Erxin Yu, Jing Li, Ming Liao, Siqi Wang, Zuchen Gao, Fei Mi, Lanqing Hong·June 25, 2024

Summary

This paper presents the CoSafe dataset, a novel resource for evaluating the safety of large language models (LLMs) in multi-turn dialogue scenarios, focusing on coreference interactions. The dataset consists of 1,400 questions across 14 categories, revealing a 56% attack success rate in LLMs, particularly LLaMA2-Chat-7b, indicating potential safety vulnerabilities. Coreference attacks in multi-turn dialogues are found to be more effective than single-prompt attacks, highlighting the need for considering dialogue context in safety assessments. The study compares various models, showing that multi-turn coreference can bypass safety mechanisms, with some models like LLaMA3 and Mistral showing improved safety due to their design. The research also emphasizes the importance of addressing safety concerns, the role of system prompts and Chain-of-Thought methods, and the need for robust defense strategies in LLMs for real-world applications.
Mind map
Safety improvements observed in specific models
LLMs examined, including LLaMA2-Chat-7b, LLaMA3, and Mistral
Effectiveness in bypassing safety mechanisms
Multi-turn vs. single-prompt coreference attacks
Discussion on addressing safety concerns in real-world applications
Evaluation of system prompts and Chain-of-Thought methods
Model Comparison
Attack Analysis
Designing multi-turn and single-prompt scenarios for comparison
Development of 1,400 questions across 14 categories
Highlight the need for context-aware safety assessments
Assess safety vulnerabilities in LLMs through coreference attacks
To introduce the CoSafe dataset
Growing concerns about safety risks, especially in coreference interactions
Emergence of large language models and their increasing use in dialogue systems
Importance of collaboration between researchers and developers in addressing safety challenges
Directions for enhancing LLM safety in multi-turn dialogue scenarios
Recommendations for future research and development in robust defense strategies
The significance of context-aware safety assessments for LLMs
Safety improvements demonstrated by LLaMA3 and Mistral
Coreference attacks' effectiveness in multi-turn dialogues
Attack success rate of 56% in LLaMA2-Chat-7b
Safety Mechanisms and Defense Strategies
Data Preprocessing
Data Collection
Objective
Background
Future Directions
Conclusion
Results
Method
Introduction
Outline
Introduction
Background
Emergence of large language models and their increasing use in dialogue systems
Growing concerns about safety risks, especially in coreference interactions
Objective
To introduce the CoSafe dataset
Assess safety vulnerabilities in LLMs through coreference attacks
Highlight the need for context-aware safety assessments
Method
Data Collection
Development of 1,400 questions across 14 categories
Designing multi-turn and single-prompt scenarios for comparison
Data Preprocessing
Coreference attack generation and implementation
Analysis of attack success rates and model performance
Attack Analysis
Multi-turn vs. single-prompt coreference attacks
Effectiveness in bypassing safety mechanisms
Model Comparison
LLMs examined, including LLaMA2-Chat-7b, LLaMA3, and Mistral
Safety improvements observed in specific models
Safety Mechanisms and Defense Strategies
Evaluation of system prompts and Chain-of-Thought methods
Discussion on addressing safety concerns in real-world applications
Results
Attack success rate of 56% in LLaMA2-Chat-7b
Coreference attacks' effectiveness in multi-turn dialogues
Safety improvements demonstrated by LLaMA3 and Mistral
Conclusion
The significance of context-aware safety assessments for LLMs
Recommendations for future research and development in robust defense strategies
Future Directions
Directions for enhancing LLM safety in multi-turn dialogue scenarios
Importance of collaboration between researchers and developers in addressing safety challenges
Key findings
6

Paper digest

What problem does the paper attempt to solve? Is this a new problem?

To provide a more accurate answer, I would need more specific information about the paper you are referring to. Please provide me with additional details or context so I can assist you better.


What scientific hypothesis does this paper seek to validate?

I would be happy to help you with that. Please provide me with the title of the paper or some context so I can better understand the scientific hypothesis it aims to validate.


What new ideas, methods, or models does the paper propose? What are the characteristics and advantages compared to previous methods?

The paper "CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference" proposes several novel ideas, methods, and models in the realm of large language model (LLM) safety evaluation . Here are some key points from the paper:

  1. Multi-Turn Dialogue Coreference Safety Evaluation: The paper introduces a new approach to studying LLM safety by focusing on multi-turn dialogue coreference attacks. It presents a dataset consisting of 1,400 questions across 14 categories, each designed to assess safety vulnerabilities in LLMs during coreference interactions .

  2. Evaluation of Five LLMs: The study evaluates five widely used open-source LLMs under multi-turn coreference safety attacks. The results reveal varying levels of vulnerability among the models, with the highest attack success rate at 56% for the LLaMA2-Chat-7b model and the lowest at 13.9% for the Mistral-7B-Instruct model .

  3. Safety Enhancement through Red-Teaming: The paper discusses the importance of safety assurances for LLMs and highlights the red-teaming approach introduced by Anthropic and DeepMind to identify and address safety loopholes in models. This approach involves role-playing attackers to enhance safety in LLMs .

  4. Dataset Release Statement: The authors emphasize that the dataset created for the study is intended for academic research purposes only and should not be misused. This statement aims to ensure responsible use of the dataset for further research and evaluation of LLM safety .

Overall, the paper contributes to the field of LLM safety evaluation by introducing a novel focus on multi-turn dialogue coreference safety attacks, evaluating the vulnerability of different LLMs, and emphasizing the importance of responsible dataset usage for academic research . The paper "CoSafe: Evaluating Large Language Model Safety in Multi-Turn Dialogue Coreference" introduces several characteristics and advantages compared to previous methods in the evaluation of large language model (LLM) safety. Here are some key points from the paper that highlight these aspects:

  1. Focus on Multi-Turn Dialogue Coreference Safety: One of the main characteristics of the paper is its focus on evaluating LLM safety in the context of multi-turn dialogue coreference attacks. This approach differs from previous methods that may have primarily focused on single-turn interactions or other types of safety vulnerabilities. By considering multi-turn dialogue coreference, the paper provides a more comprehensive assessment of LLM safety in complex conversational settings .

  2. Creation of a New Dataset: The paper introduces a new dataset consisting of 1,400 questions across 14 categories specifically designed to evaluate LLM safety in multi-turn dialogue coreference scenarios. This dataset is a novel contribution that enables researchers to assess the robustness of LLMs in handling coreference challenges during conversational interactions. Previous methods may not have had access to such specialized datasets tailored for this specific evaluation purpose .

  3. Evaluation of Multiple LLMs: The study evaluates five different LLMs under multi-turn coreference safety attacks, providing a comparative analysis of their vulnerability levels. By testing multiple models, the paper offers insights into the varying performance and susceptibility of different LLMs to coreference attacks. This comparative evaluation enhances the understanding of model behavior and highlights potential areas for improvement in LLM safety mechanisms .

  4. Red-Teaming Approach for Safety Enhancement: The paper discusses the red-teaming approach introduced by Anthropic and DeepMind as a method to enhance LLM safety. This approach involves simulating adversarial attacks to identify and address safety vulnerabilities in models. By incorporating red-teaming strategies, the paper advances the field by promoting proactive measures to improve LLM safety and resilience against potential threats .

  5. Responsible Dataset Usage: The authors emphasize responsible dataset usage by providing a release statement that underscores the intended academic research purposes of the dataset. This commitment to ethical research practices ensures that the dataset is used appropriately and prevents misuse that could compromise the integrity of LLM safety evaluations. This emphasis on responsible data usage sets a standard for future studies in the field .

Overall, the characteristics and advantages of the paper "CoSafe" lie in its innovative focus on multi-turn dialogue coreference safety evaluation, the creation of a specialized dataset, the comparative analysis of multiple LLMs, the adoption of red-teaming strategies for safety enhancement, and the promotion of responsible dataset usage for academic research purposes .


Do any related researches exist? Who are the noteworthy researchers on this topic in this field?What is the key to the solution mentioned in the paper?

In the field of evaluating large language model safety in multi-turn dialogue coreference, several related research works exist by notable researchers. Some of the noteworthy researchers in this field include Heeyoung Lee, Yves Peirsman, Angel Chang, Nathanael Chambers, Mihai Surdeanu, Dan Jurafsky, Kenton Lee, Luheng He, Mike Lewis, Luke Zettlemoyer, Stephanie Lin, Jacob Hilton, Owain Evans, Niloofar Mireshghallah, Hyunwoo Kim, Xuhui Zhou, Yulia Tsvetkov, Maarten Sap, Reza Shokri, Yejin Choi, Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Thompson, Phu Mon Htut, and Samuel Bowman .

The key to the solution mentioned in the paper involves testing the privacy implications of language models via contextual integrity theory .


How were the experiments in the paper designed?

To provide you with a detailed answer, I would need more specific information about the paper you are referring to. Could you please provide me with the title of the paper or some key details about the experiments so I can assist you better?


What is the dataset used for quantitative evaluation? Is the code open source?

The dataset used for quantitative evaluation in the study is CoSafe . The code for the evaluation is open source and can be accessed at the following link: https://huggingface.co/PKU-Alignment/beaver-dam-7b .


Do the experiments and results in the paper provide good support for the scientific hypotheses that need to be verified? Please analyze.

The experiments and results presented in the paper provide substantial support for the scientific hypotheses that require verification. The study includes evaluations from multiple perspectives, such as GPT-4 Harmless Score, QA Moderation, and Human Evaluation, which collectively assess the safety and reliability of the models used in multi-turn dialogue . These evaluations contribute to a comprehensive understanding of the effectiveness and safety of the large language models in dialogue contexts, aligning with the scientific hypotheses under scrutiny. The consideration of various factors like accuracy, clarity, completeness, contextual understanding, and user engagement in the evaluations enhances the robustness of the study's findings . The results obtained from these evaluations offer valuable insights into the performance and safety aspects of the models, thereby supporting the scientific hypotheses being investigated in the paper.


What are the contributions of this paper?

The paper makes several contributions, including:

  • Evaluation and mitigation of safety risks in Large Language Models (LLMs) .
  • Providing a dataset intended for academic research purposes only to prevent misuse .
  • Support from various funding sources such as Huawei Noah’s Ark Lab, NSFC Young Scientists Fund, Research Grants Council of Hong Kong, and others .
  • References to related works in the field of coreference resolution and language model behaviors .

What work can be continued in depth?

Work that can be continued in depth typically involves projects or tasks that require further analysis, research, or development. This could include:

  1. Research projects that require more data collection, analysis, and interpretation.
  2. Complex problem-solving tasks that need further exploration and experimentation.
  3. Creative projects that can be expanded upon with more ideas and iterations.
  4. Skill development activities that require continuous practice and improvement.
  5. Long-term projects that need ongoing monitoring and adjustments.

If you have a specific type of work in mind, feel free to provide more details so I can give you a more tailored response.

Tables
2
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