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

• URL: http://arxiv.org/abs/2406.10802v1
• Date: Sun, 16 Jun 2024 04:48:43 GMT
• Title: KGPA: Robustness Evaluation for Large Language Models via Cross-Domain Knowledge Graphs
• Authors: Aihua Pei, Zehua Yang, Shunan Zhu, Ruoxi Cheng, Ju Jia, Lina Wang,
• Abstract summary: This paper proposes a framework that systematically evaluates the robustness of large language models under adversarial attack scenarios. Our framework generates original prompts from the triplets of knowledge graphs and creates adversarial prompts by poisoning. Experiments show that adversarial robustness of the ChatGPT family ranks as GPT-4-turbo > GPT-4o > GPT-3.5-turbo, and the robustness of large language models is influenced by the professional domains in which they operate.
• Score: 5.798411590796167
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing frameworks for assessing robustness of large language models (LLMs) overly depend on specific benchmarks, increasing costs and failing to evaluate performance of LLMs in professional domains due to dataset limitations. This paper proposes a framework that systematically evaluates the robustness of LLMs under adversarial attack scenarios by leveraging knowledge graphs (KGs). Our framework generates original prompts from the triplets of knowledge graphs and creates adversarial prompts by poisoning, assessing the robustness of LLMs through the results of these adversarial attacks. We systematically evaluate the effectiveness of this framework and its modules. Experiments show that adversarial robustness of the ChatGPT family ranks as GPT-4-turbo > GPT-4o > GPT-3.5-turbo, and the robustness of large language models is influenced by the professional domains in which they operate.

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