用逻辑编程提升大模型法律推理能力，让普通人也能低成本获取专业法律服务。
Equitable Access to Justice: Logical LLMs Show Promise
- 将法律条文转化为逻辑程序，增强大模型的系统性推理能力。
- o1-preview模型成功解析健康保险合同，而GPT-4o失败。
- 适合法律科技开发者与司法普惠研究者参考。
美国司法系统的高昂成本与复杂性限制了民众获取法律帮助的机会。大型语言模型（LLMs）在提升司法可及性方面潜力巨大，但在法律场景中应用时，其一致性和可靠性要求催生对系统2推理的需求。本文探索将大模型与逻辑编程结合，以增强其推理能力，使其更接近专业律师的战略思维。目标是将法律与合同文本转化为可应用于具体案件的逻辑程序，重点聚焦于保险合同。实验表明，GPT-4o无法将一份简单健康保险合同编码为逻辑代码，而新发布的OpenAI o1-preview模型则成功实现，展示了具备高级系统2推理能力的大模型在拓展司法可及性方面的前景。
原文摘要 · Abstract (English)
The costs and complexity of the American judicial system limit access to legal solutions for many Americans. Large language models (LLMs) hold great potential to improve access to justice. However, a major challenge in applying AI and LLMs in legal contexts, where consistency and reliability are crucial, is the need for System 2 reasoning. In this paper, we explore the integration of LLMs with logic programming to enhance their ability to reason, bringing their strategic capabilities closer to that of a skilled lawyer. Our objective is to translate laws and contracts into logic programs that can be applied to specific legal cases, with a focus on insurance contracts. We demonstrate that while GPT-4o fails to encode a simple health insurance contract into logical code, the recently released OpenAI o1-preview model succeeds, exemplifying how LLMs with advanced System 2 reasoning capabilities can expand access to justice.
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