KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs

Aihua Pei, Zehua Yang, Shunan Zhu, Ruoxi Cheng, Ju Jia, Lina Wang·June 16, 2024

Summary

This paper presents the Knowledge Graph Based PromptAttack (KGPA) framework, a novel method for evaluating the robustness of large language models (LLMs) against adversarial attacks. KGPA uses knowledge graphs to generate prompts and assess performance across various domains, including professional ones, differentiating from existing methods that rely on costly and domain-specific benchmarks. The study finds that GPT-4-turbo exhibits the highest adversarial robustness among tested models, with robustness influenced by the domain of the knowledge graph. The framework employs modules for prompt generation, adversarial production, and optimization, and explores the impact of knowledge scope on model resilience. It reveals that LLMs have varying robustness based on the type of knowledge in prompts, with GPT-4o offering a trade-off between efficiency and robustness. The paper evaluates multiple models and datasets, highlighting the importance of model design, training data, and the need for practical and adaptable evaluation methods. Future research will expand to other problem types and address limitations in scoring methods and biases.

Key findings

7

Paper digest

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

The paper aims to address the issue of evaluating the robustness of large language models (LLMs) under adversarial attack scenarios by leveraging knowledge graphs (KGs) . This framework systematically generates original prompts from KG triplets and creates adversarial prompts to assess the robustness of LLMs through the results of these attacks . The research proposes a new framework that efficiently generates original and adversarial prompts from KG triplets without relying on specially constructed benchmark datasets, contributing to the study of LLM robustness evaluation and adversarial attacks . This problem of evaluating LLM robustness under adversarial attacks using KGs is a new approach that offers a systematic way to assess the performance of LLMs in professional domains .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that leveraging knowledge graphs (KGs) to generate original prompts and create adversarial prompts can systematically evaluate the robustness of large language models (LLMs) under adversarial attack scenarios . The framework proposed in the paper assesses the effectiveness of this approach and its modules, demonstrating how the robustness of LLMs, such as the ChatGPT family, can be ranked based on their performance under adversarial attacks .


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

The paper proposes a new framework called KGPA for evaluating the robustness of large language models (LLMs) through adversarial attacks . This framework efficiently generates original and adversarial prompts from knowledge graph triplets without relying on specially constructed benchmark datasets . The KGPA framework includes modules for generating original prompts, optimizing adversarial prompts, and selecting effective prompts for attacks, with a focus on how different settings within the modules impact the results of adversarial attacks and robustness evaluations .

One key aspect of the proposed framework is the Few-Shot Attack Strategy, which has gained attention due to the increasing use of LLMs in various tasks . This strategy aims to evaluate and improve the models' ability to resist attacks by utilizing adversarial samples that can cause models to make incorrect predictions . The strategy is designed to efficiently execute attacks using a small number of samples, revealing potential weaknesses in models even with limited data .

The methodology section of the paper introduces the Knowledge Graph based PromptAttack (KGPA) framework, which involves converting knowledge graph triplets into original prompts, modifying them into adversarial prompts, selecting suitable prompts for attacks, and evaluating LLM robustness using specific metrics . The framework leverages facts stored in knowledge graphs organized as triplets (Subject, Predicate, Object) to automatically generate original prompts with labels indicating correctness or errors in the prompts .

Additionally, the paper discusses the PRE Module within the KGPA framework, which filters qualified adversarial prompts based on the large language model's self-assessment score, LLMScore . The module aims to refine the scoring criteria and evaluate appropriate threshold settings to tailor the LLMScore for the filtering tasks . This module plays a crucial role in assessing the robustness of LLMs to adversarial attacks . The KGPA framework introduces several key characteristics and advantages compared to previous methods for evaluating the robustness of large language models (LLMs) through adversarial attacks .

  1. Knowledge Graph Utilization: KGPA leverages knowledge graphs to generate original prompts and create adversarial prompts for robustness evaluation, offering a systematic approach to assess LLMs under adversarial attack scenarios . This utilization of knowledge graphs enhances the generation of diverse and challenging adversarial samples, contributing to a more comprehensive evaluation of model robustness .

  2. Few-Shot Attack Strategy: The framework incorporates a Few-Shot Attack Strategy, which efficiently executes attacks using a small number of samples to reveal potential weaknesses in models, even with limited data . This strategy is particularly valuable for evaluating newly emerging LLMs that typically require large amounts of data for training and testing .

  3. Module Efficiency: KGPA includes modules such as T2P, KGB-FSA, PRE, and APGP, each serving specific functions in the framework . For instance, the T2P module generates original prompts by converting knowledge graph triplets, while the PRE module filters out low-quality adversarial prompts based on the large language model's self-assessment score, LLMScore .

  4. Effectiveness in Adversarial Attacks: KGPA demonstrates effectiveness in generating challenging adversarial samples and robustness evaluation, offering a broad spectrum for assessing model robustness . The framework achieves higher ASR values with Few-Shot Attack Strategy, indicating its capability to generate more successful adversarial samples .

  5. Cost Efficiency: Unlike some existing frameworks that rely on manually annotated benchmark datasets, KGPA reduces costs by using automatically constructed knowledge graph datasets, making it more cost-effective and applicable across various domains .

In summary, the KGPA framework stands out for its systematic approach, utilization of knowledge graphs, incorporation of Few-Shot Attack Strategy, module efficiency, effectiveness in adversarial attacks, and cost efficiency compared to previous methods for evaluating the robustness of large language models .


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?

Several related research works exist in the field of large language models and knowledge graphs. Noteworthy researchers in this area include Timothy Niven, Hung-Yu Kao, Fabio Petroni, Mujeen Sung, Jinhyuk Lee, and many others . The key solution mentioned in the paper is a framework proposed for evaluating the robustness of large language models under adversarial attack scenarios by leveraging knowledge graphs. This framework generates original prompts from knowledge graph triplets and creates adversarial prompts to assess the robustness of the models through the results of these attacks .


How were the experiments in the paper designed?

The experiments in the paper were designed by dividing the knowledge graph dataset into two categories: general domain knowledge graph datasets and specialized domain knowledge graph datasets . The general domain knowledge graph datasets included T-REx and Google-RE, while the specialized domain knowledge graph datasets included UMLS and WikiBio . Different large language models from the ChatGPT family, such as GPT-3.5-turbo, GPT-4-turbo, and GPT-4o, were utilized for the experiments . The experiments involved generating original prompts from knowledge graph triplets and creating adversarial prompts to evaluate the robustness of large language models through specific metrics . The framework systematically evaluated the robustness of large language models under adversarial attack scenarios by leveraging knowledge graphs .


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

The dataset used for quantitative evaluation in the KGPA framework includes general domain knowledge graph datasets like T-REx and Google-RE, as well as specialized domain knowledge graph datasets such as UMLS and WikiBio . Regarding the code, the information provided does not specify whether the code used in the KGPA framework for robustness evaluation of large language models is open source or not.


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 need to be verified. The study conducted a comprehensive evaluation of large language models (LLMs) using various knowledge graph datasets and different prompt generation strategies, including template-based and LLM-based approaches . The experiments involved models like GPT-3.5-turbo, GPT-4-turbo, and GPT-4o, assessing aspects such as Adversarial Success Rate (ASR), Normal Response Accuracy (NRA), and Robust Response Accuracy (RRA) .

The results from the experiments demonstrate the effectiveness of the models in handling adversarial prompts and the robustness of the models under different conditions . For instance, GPT-4-turbo consistently maintained higher accuracy than GPT-4o across various datasets, indicating superior robustness in responding to adversarial prompts . The comparison of RRA values between the models on different knowledge graphs further highlights the relative strengths and robustness of the models .

Moreover, the study analyzed the responses of the LLMs under normal and adversarial conditions, revealing that some prompts may elicit different responses in these scenarios, indicating inherent randomness in LLM responses . This analysis underscores the complexity of evaluating LLMs and the need to consider various factors when assessing their performance .

Overall, the experiments and results in the paper provide a solid foundation for verifying scientific hypotheses related to the robustness and performance of large language models when interacting with knowledge graphs and responding to different types of prompts . The detailed evaluation metrics and comparisons offer valuable insights into the capabilities and limitations of LLMs in handling diverse scenarios, contributing significantly to the understanding of these models in real-world applications.


What are the contributions of this paper?

The contributions of the paper "KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs" include proposing a framework for systematically evaluating the robustness of large language models under adversarial attack scenarios by leveraging knowledge graphs (KGs) . This framework generates original prompts from the triplets of knowledge graphs and creates adversarial prompts through poisoning, enabling the assessment of LLMs' robustness based on the outcomes of these adversarial attacks . The paper systematically evaluates the effectiveness of this framework and its modules, demonstrating that the adversarial robustness of the ChatGPT family ranks as GPT-4-turbo > GPT-4o > GPT-3.5-turbo . Additionally, it highlights that the robustness of large language models is influenced by the professional domains in which they operate .


What work can be continued in depth?

To further enhance the evaluation of Large Language Models (LLMs) in terms of robustness, future research can focus on enriching the types of problems used to assess LLM robustness. Currently, the KGPA framework primarily evaluates LLMs through classification tasks, but expanding the evaluation to include other types of problems such as short answer questions and true/false questions would provide a more comprehensive assessment of LLM robustness . This broader evaluation approach can offer insights into the performance of LLMs across different types of tasks, leading to a more thorough understanding of their robustness capabilities.

Tables

1

Introduction
Background
Evolution of adversarial attacks on LLMs
Limitations of existing evaluation methods
Objective
To develop a novel framework for robustness assessment
To analyze model resilience across domains and knowledge types
Method
Prompt Generation
Knowledge Graph Integration
Utilizing knowledge graphs for prompt creation
Diverse domain coverage
Prompt Design Principles
Selection and modification of prompts
Adversarial Production
Attack Strategies
Generation of adversarial prompts
Comparison with existing attack methods
Adversarial Optimization
Techniques for enhancing attack effectiveness
Evaluation
Model Performance Analysis
Testing on multiple LLMs, including GPT-4-turbo and GPT-4o
Robustness across different domains
Impact of Knowledge Scope
Exploration of varying knowledge types on resilience
Results and Findings
GPT-4-turbo's highest robustness
Trade-off between efficiency and robustness in GPT-4o
Influence of training data and model design
Discussion
Limitations and Future Directions
Current evaluation scope and problem types
Improving scoring methods and addressing biases
Potential for practical applications
Implications for Large Language Model Development
Recommendations for enhancing robustness in future models
Conclusion
Summary of key findings and contributions
The significance of KGPA for evaluating and enhancing LLM robustness
Call for further research in the field
Basic info
papers
computation and language
artificial intelligence
Advanced features
Insights
How does KGPA differ from existing methods for evaluating LLMs' adversarial robustness?
Which model demonstrated the highest adversarial robustness according to the study?
What is the primary focus of the Knowledge Graph Based PromptAttack (KGPA) framework?
What does the research suggest about the impact of knowledge scope on LLMs' resilience?

KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs

Aihua Pei, Zehua Yang, Shunan Zhu, Ruoxi Cheng, Ju Jia, Lina Wang·June 16, 2024

Summary

This paper presents the Knowledge Graph Based PromptAttack (KGPA) framework, a novel method for evaluating the robustness of large language models (LLMs) against adversarial attacks. KGPA uses knowledge graphs to generate prompts and assess performance across various domains, including professional ones, differentiating from existing methods that rely on costly and domain-specific benchmarks. The study finds that GPT-4-turbo exhibits the highest adversarial robustness among tested models, with robustness influenced by the domain of the knowledge graph. The framework employs modules for prompt generation, adversarial production, and optimization, and explores the impact of knowledge scope on model resilience. It reveals that LLMs have varying robustness based on the type of knowledge in prompts, with GPT-4o offering a trade-off between efficiency and robustness. The paper evaluates multiple models and datasets, highlighting the importance of model design, training data, and the need for practical and adaptable evaluation methods. Future research will expand to other problem types and address limitations in scoring methods and biases.
Mind map
Exploration of varying knowledge types on resilience
Robustness across different domains
Testing on multiple LLMs, including GPT-4-turbo and GPT-4o
Techniques for enhancing attack effectiveness
Comparison with existing attack methods
Generation of adversarial prompts
Selection and modification of prompts
Diverse domain coverage
Utilizing knowledge graphs for prompt creation
Recommendations for enhancing robustness in future models
Potential for practical applications
Improving scoring methods and addressing biases
Current evaluation scope and problem types
Influence of training data and model design
Trade-off between efficiency and robustness in GPT-4o
GPT-4-turbo's highest robustness
Impact of Knowledge Scope
Model Performance Analysis
Adversarial Optimization
Attack Strategies
Prompt Design Principles
Knowledge Graph Integration
To analyze model resilience across domains and knowledge types
To develop a novel framework for robustness assessment
Limitations of existing evaluation methods
Evolution of adversarial attacks on LLMs
Call for further research in the field
The significance of KGPA for evaluating and enhancing LLM robustness
Summary of key findings and contributions
Implications for Large Language Model Development
Limitations and Future Directions
Results and Findings
Evaluation
Adversarial Production
Prompt Generation
Objective
Background
Conclusion
Discussion
Method
Introduction
Outline
Introduction
Background
Evolution of adversarial attacks on LLMs
Limitations of existing evaluation methods
Objective
To develop a novel framework for robustness assessment
To analyze model resilience across domains and knowledge types
Method
Prompt Generation
Knowledge Graph Integration
Utilizing knowledge graphs for prompt creation
Diverse domain coverage
Prompt Design Principles
Selection and modification of prompts
Adversarial Production
Attack Strategies
Generation of adversarial prompts
Comparison with existing attack methods
Adversarial Optimization
Techniques for enhancing attack effectiveness
Evaluation
Model Performance Analysis
Testing on multiple LLMs, including GPT-4-turbo and GPT-4o
Robustness across different domains
Impact of Knowledge Scope
Exploration of varying knowledge types on resilience
Results and Findings
GPT-4-turbo's highest robustness
Trade-off between efficiency and robustness in GPT-4o
Influence of training data and model design
Discussion
Limitations and Future Directions
Current evaluation scope and problem types
Improving scoring methods and addressing biases
Potential for practical applications
Implications for Large Language Model Development
Recommendations for enhancing robustness in future models
Conclusion
Summary of key findings and contributions
The significance of KGPA for evaluating and enhancing LLM robustness
Call for further research in the field
Key findings
7

Paper digest

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

The paper aims to address the issue of evaluating the robustness of large language models (LLMs) under adversarial attack scenarios by leveraging knowledge graphs (KGs) . This framework systematically generates original prompts from KG triplets and creates adversarial prompts to assess the robustness of LLMs through the results of these attacks . The research proposes a new framework that efficiently generates original and adversarial prompts from KG triplets without relying on specially constructed benchmark datasets, contributing to the study of LLM robustness evaluation and adversarial attacks . This problem of evaluating LLM robustness under adversarial attacks using KGs is a new approach that offers a systematic way to assess the performance of LLMs in professional domains .


What scientific hypothesis does this paper seek to validate?

This paper aims to validate the scientific hypothesis that leveraging knowledge graphs (KGs) to generate original prompts and create adversarial prompts can systematically evaluate the robustness of large language models (LLMs) under adversarial attack scenarios . The framework proposed in the paper assesses the effectiveness of this approach and its modules, demonstrating how the robustness of LLMs, such as the ChatGPT family, can be ranked based on their performance under adversarial attacks .


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

The paper proposes a new framework called KGPA for evaluating the robustness of large language models (LLMs) through adversarial attacks . This framework efficiently generates original and adversarial prompts from knowledge graph triplets without relying on specially constructed benchmark datasets . The KGPA framework includes modules for generating original prompts, optimizing adversarial prompts, and selecting effective prompts for attacks, with a focus on how different settings within the modules impact the results of adversarial attacks and robustness evaluations .

One key aspect of the proposed framework is the Few-Shot Attack Strategy, which has gained attention due to the increasing use of LLMs in various tasks . This strategy aims to evaluate and improve the models' ability to resist attacks by utilizing adversarial samples that can cause models to make incorrect predictions . The strategy is designed to efficiently execute attacks using a small number of samples, revealing potential weaknesses in models even with limited data .

The methodology section of the paper introduces the Knowledge Graph based PromptAttack (KGPA) framework, which involves converting knowledge graph triplets into original prompts, modifying them into adversarial prompts, selecting suitable prompts for attacks, and evaluating LLM robustness using specific metrics . The framework leverages facts stored in knowledge graphs organized as triplets (Subject, Predicate, Object) to automatically generate original prompts with labels indicating correctness or errors in the prompts .

Additionally, the paper discusses the PRE Module within the KGPA framework, which filters qualified adversarial prompts based on the large language model's self-assessment score, LLMScore . The module aims to refine the scoring criteria and evaluate appropriate threshold settings to tailor the LLMScore for the filtering tasks . This module plays a crucial role in assessing the robustness of LLMs to adversarial attacks . The KGPA framework introduces several key characteristics and advantages compared to previous methods for evaluating the robustness of large language models (LLMs) through adversarial attacks .

  1. Knowledge Graph Utilization: KGPA leverages knowledge graphs to generate original prompts and create adversarial prompts for robustness evaluation, offering a systematic approach to assess LLMs under adversarial attack scenarios . This utilization of knowledge graphs enhances the generation of diverse and challenging adversarial samples, contributing to a more comprehensive evaluation of model robustness .

  2. Few-Shot Attack Strategy: The framework incorporates a Few-Shot Attack Strategy, which efficiently executes attacks using a small number of samples to reveal potential weaknesses in models, even with limited data . This strategy is particularly valuable for evaluating newly emerging LLMs that typically require large amounts of data for training and testing .

  3. Module Efficiency: KGPA includes modules such as T2P, KGB-FSA, PRE, and APGP, each serving specific functions in the framework . For instance, the T2P module generates original prompts by converting knowledge graph triplets, while the PRE module filters out low-quality adversarial prompts based on the large language model's self-assessment score, LLMScore .

  4. Effectiveness in Adversarial Attacks: KGPA demonstrates effectiveness in generating challenging adversarial samples and robustness evaluation, offering a broad spectrum for assessing model robustness . The framework achieves higher ASR values with Few-Shot Attack Strategy, indicating its capability to generate more successful adversarial samples .

  5. Cost Efficiency: Unlike some existing frameworks that rely on manually annotated benchmark datasets, KGPA reduces costs by using automatically constructed knowledge graph datasets, making it more cost-effective and applicable across various domains .

In summary, the KGPA framework stands out for its systematic approach, utilization of knowledge graphs, incorporation of Few-Shot Attack Strategy, module efficiency, effectiveness in adversarial attacks, and cost efficiency compared to previous methods for evaluating the robustness of large language models .


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?

Several related research works exist in the field of large language models and knowledge graphs. Noteworthy researchers in this area include Timothy Niven, Hung-Yu Kao, Fabio Petroni, Mujeen Sung, Jinhyuk Lee, and many others . The key solution mentioned in the paper is a framework proposed for evaluating the robustness of large language models under adversarial attack scenarios by leveraging knowledge graphs. This framework generates original prompts from knowledge graph triplets and creates adversarial prompts to assess the robustness of the models through the results of these attacks .


How were the experiments in the paper designed?

The experiments in the paper were designed by dividing the knowledge graph dataset into two categories: general domain knowledge graph datasets and specialized domain knowledge graph datasets . The general domain knowledge graph datasets included T-REx and Google-RE, while the specialized domain knowledge graph datasets included UMLS and WikiBio . Different large language models from the ChatGPT family, such as GPT-3.5-turbo, GPT-4-turbo, and GPT-4o, were utilized for the experiments . The experiments involved generating original prompts from knowledge graph triplets and creating adversarial prompts to evaluate the robustness of large language models through specific metrics . The framework systematically evaluated the robustness of large language models under adversarial attack scenarios by leveraging knowledge graphs .


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

The dataset used for quantitative evaluation in the KGPA framework includes general domain knowledge graph datasets like T-REx and Google-RE, as well as specialized domain knowledge graph datasets such as UMLS and WikiBio . Regarding the code, the information provided does not specify whether the code used in the KGPA framework for robustness evaluation of large language models is open source or not.


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 need to be verified. The study conducted a comprehensive evaluation of large language models (LLMs) using various knowledge graph datasets and different prompt generation strategies, including template-based and LLM-based approaches . The experiments involved models like GPT-3.5-turbo, GPT-4-turbo, and GPT-4o, assessing aspects such as Adversarial Success Rate (ASR), Normal Response Accuracy (NRA), and Robust Response Accuracy (RRA) .

The results from the experiments demonstrate the effectiveness of the models in handling adversarial prompts and the robustness of the models under different conditions . For instance, GPT-4-turbo consistently maintained higher accuracy than GPT-4o across various datasets, indicating superior robustness in responding to adversarial prompts . The comparison of RRA values between the models on different knowledge graphs further highlights the relative strengths and robustness of the models .

Moreover, the study analyzed the responses of the LLMs under normal and adversarial conditions, revealing that some prompts may elicit different responses in these scenarios, indicating inherent randomness in LLM responses . This analysis underscores the complexity of evaluating LLMs and the need to consider various factors when assessing their performance .

Overall, the experiments and results in the paper provide a solid foundation for verifying scientific hypotheses related to the robustness and performance of large language models when interacting with knowledge graphs and responding to different types of prompts . The detailed evaluation metrics and comparisons offer valuable insights into the capabilities and limitations of LLMs in handling diverse scenarios, contributing significantly to the understanding of these models in real-world applications.


What are the contributions of this paper?

The contributions of the paper "KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs" include proposing a framework for systematically evaluating the robustness of large language models under adversarial attack scenarios by leveraging knowledge graphs (KGs) . This framework generates original prompts from the triplets of knowledge graphs and creates adversarial prompts through poisoning, enabling the assessment of LLMs' robustness based on the outcomes of these adversarial attacks . The paper systematically evaluates the effectiveness of this framework and its modules, demonstrating that the adversarial robustness of the ChatGPT family ranks as GPT-4-turbo > GPT-4o > GPT-3.5-turbo . Additionally, it highlights that the robustness of large language models is influenced by the professional domains in which they operate .


What work can be continued in depth?

To further enhance the evaluation of Large Language Models (LLMs) in terms of robustness, future research can focus on enriching the types of problems used to assess LLM robustness. Currently, the KGPA framework primarily evaluates LLMs through classification tasks, but expanding the evaluation to include other types of problems such as short answer questions and true/false questions would provide a more comprehensive assessment of LLM robustness . This broader evaluation approach can offer insights into the performance of LLMs across different types of tasks, leading to a more thorough understanding of their robustness capabilities.

Tables
1
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