用提示工程和法律知识图谱防范大模型回答的法律风险
A Prompt Engineering Approach and a Knowledge Graph based Framework for Tackling Legal Implications of Large Language Model Answers
- 通过重写提示词识别潜在法律问题
- 实证发现多款大模型存在法律风险，需即时干预
- 结合法律知识图谱生成引用，适合法律AI研究者
随着大语言模型（LLMs）日益普及，用户可能盲目信任其回复，即便模型建议的行为存在潜在法律风险，仍可能使用户陷入危险。我们对多个现有LLM进行了实证分析，揭示该问题的紧迫性。为此，我们提出一种短期解决方案：通过提示工程（prompt engineering）隔离法律问题。同时分析了该方法的效果与局限，并强调需依赖额外资源才能彻底解决。此外，我们提出一个基于法律知识图谱（KG）的框架，可为模型回答生成法律引文，增强回应的法律依据。
原文摘要 · Abstract (English)
With the recent surge in popularity of Large Language Models (LLMs), there is the rising risk of users blindly trusting the information in the response, even in cases where the LLM recommends actions that have potential legal implications and this may put the user in danger. We provide an empirical analysis on multiple existing LLMs showing the urgency of the problem. Hence, we propose a short-term solution consisting in an approach for isolating these legal issues through prompt re-engineering. We further analyse the outcomes but also the limitations of the prompt engineering based approach and we highlight the need of additional resources for fully solving the problem We also propose a framework powered by a legal knowledge graph (KG) to generate legal citations for these legal issues, enriching the response of the LLM.
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