逻辑租约诉讼：用于纽约租赁法律合规的 Prolog 与 LLM
Logical Lease Litigation: Prolog and LLMs for Rental Law Compliance in New York
AI总结：
本文提出 LogicLease 系统，结合 LLM 信息提取与 Prolog 法律推理，自动分析纽约房东—租户案件并引用法律，实现透明、可控且无幻觉的合规判断。
摘要：
法律案件需要依据法律进行严谨的逻辑推理，而与非技术用户的交互则必须采用自然语言。作为结合使用 Prolog 进行逻辑推理与使用大型语言模型（LLM）进行自然语言处理的应用，本文提出了一种新颖的方法与系统 LogicLease，用于自动化分析纽约州的房东—租户法律案件。LogicLease 通过分析案件描述并引用所有相关法律，判定其是否符合相关法律要求。该系统利用 LLM 进行信息提取，并利用 Prolog 进行法律推理。通过将信息提取与法律推理分离，LogicLease 对适用于每个案件的法律逻辑实现了更高的透明度和控制力。我们通过一系列测试评估了 LogicLease 的准确性、效率和鲁棒性，达到了 100% 的准确率，平均处理时间为 2.57 秒。与最先进的基于 LLM 的法律分析系统相比，LogicLease 的优势在于提供清晰的逐步推理、引用具体法律，并以避免幻觉的能力脱颖而出——幻觉是 LLM 中的常见问题。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
