Using LLMs to Discover Legal Factors
원문 초록
Factors are a foundational component of legal analysis and computational models of legal reasoning. These factor-based representations enable lawyers, judges, and AI and Law researchers to reason about legal cases. In this paper, we introduce a methodology that leverages large language models (LLMs) to discover lists of factors that effectively represent a legal domain. Our method takes as input raw court opinions and produces a set of factors and associated definitions. We demonstrate that a semi-automated approach, incorporating minimal human involvement, produces factor representations that can predict case outcomes with moderate success, if not yet as well as expert-defined factors can.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Can ChatGPT Perform Reasoning Using the IRAC Method in Analyzing Legal Scenarios Like a Lawyer?
LLM-based HSE Compliance Assessment: Benchmark, Performance, and Advancements
Incorporating Legal Structure in Retrieval-Augmented Generation: A Case Study on Copyright Fair Use
CaseGen: A Benchmark for Multi-Stage Legal Case Documents Generation
JUREX-4E: Juridical Expert-Annotated Four-Element Knowledge Base for Legal Reasoning
NitiBench: A Comprehensive Studies of LLM Frameworks Capabilities for Thai Legal Question Answering
토론
이 논문과 연결된 포럼 글이 아직 없습니다.