知识注入的法律智慧：通过诊断与正无标记强化学习引导大语言模型咨询
Knowledge-Infused Legal Wisdom: Navigating LLM Consultation through the Lens of Diagnostics and Positive-Unlabeled Reinforcement Learning
AI总结：
本文提出诊断式法律大语言模型D3LM，通过自适应诊断问题、图基正无标记强化学习PURL和LLM停止准则实现精确法院意见生成，并构建新CVG数据集，在法律领域超越经典LLMs。
摘要：
生成式大语言模型（LLMs）因其广泛且多功能的特性，在包括法律领域在内的各种应用中的集成得以加速。然而，当面对法律案件时，没有法律背景的用户往往难以提出专业性的查询，并可能在向LLMs陈述案件叙述时无意中忽略关键的法律因素。为解决这一问题，我们提出了诊断式法律大语言模型（D3LM），该模型利用自适应的类律师诊断问题来收集额外的案件信息，然后提供高质量的反馈。D3LM融合了一种创新的基于图的正无标记强化学习（PURL）算法，能够生成关键问题并增强用户与LLM的交互。此外，一个集成的基于LLM的停止准则促进了精确的法院意见生成（CVG）。我们的研究还引入了一个基于美国判例法数据库的新的英文CVG数据集，为LLM研究和部署领域增添了一个重要维度。D3LM在法律领域以出色的性能和卓越的用户体验超越了经典LLMs。
英文摘要：
The integration of generative Large Language Models (LLMs) into various applications, including the legal domain, has been accelerated by their expansive and versatile nature. However, when facing a legal case, users without a legal background often struggle to formulate professional queries and may inadvertently overlook critical legal factors when presenting their case narrative to LLMs. To address this issue, we propose the Diagnostic Legal Large Language Model (D3LM), which utilizes adaptive lawyer-like diagnostic questions to collect additional case information and then provides high-quality feedback. D3LM incorporates an innovative graph-based Positive-Unlabeled Reinforcement Learning (PURL) algorithm, enabling the generation of critical questions and enhancing user-LLM interactions. Moreover, an integrated LLM-based stopping criterion facilitates precise Court Views Generation (CVG). Our research also introduces a new English-language CVG dataset based on the US case law database, enriching the realm of LLM research and deployment with a vital dimension. D3LM surpasses classical LLMs by delivering outstanding performance and a remarkable user experience in the legal domain.
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。
