用逻辑编程+大模型分析纽约租房纠纷，自动判断是否合规
Logical Lease Litigation: Prolog and LLMs for Rental Law Compliance in New York
- 用LLM提取案件信息，Prolog进行法律推理，分步可解释
- 测试中准确率100%，平均处理仅2.57秒
- 避免大模型幻觉，适合非专业人士使用
法律案件需严谨的逻辑推理，同时与非专业人士沟通需自然语言。本文提出新系统LogicLease，结合Prolog逻辑编程与大语言模型（LLMs）技术，自动化分析纽约州房东-租客法律案件。该系统通过分析案件描述，判断是否符合相关法律规定，并引用具体法律条文。LogicLease利用LLMs进行信息抽取，采用Prolog实现法律推理，通过分离信息提取与推理环节，提升法律逻辑的透明度与可控性。我们在一系列测试中评估其准确性、效率与鲁棒性，结果达到100%准确率，平均处理时间为2.57秒。相比现有基于LLM的法律分析系统，LogicLease能提供清晰的分步推理过程，准确引用法律条文，有效避免大模型常见的幻觉问题。
原文摘要 · Abstract (English)
Legal cases require careful logical reasoning following the laws, whereas interactions with non-technical users must be in natural language. As an application combining logical reasoning using Prolog and natural language processing using large language models (LLMs), this paper presents a novel approach and system, LogicLease, to automate the analysis of landlord-tenant legal cases in the state of New York. LogicLease determines compliance with relevant legal requirements by analyzing case descriptions and citing all relevant laws. It leverages LLMs for information extraction and Prolog for legal reasoning. By separating information extraction from legal reasoning, LogicLease achieves greater transparency and control over the legal logic applied to each case. We evaluate the accuracy, efficiency, and robustness of LogicLease through a series of tests, achieving 100% accuracy and an average processing time of 2.57 seconds. LogicLease presents advantages over state-of-the-art LLM-based legal analysis systems by providing clear, step-by-step reasoning, citing specific laws, and distinguishing itself by its ability to avoid hallucinations -- a common issue in LLMs.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。