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| Title: | 基於大型語言模型的法律案由分類框架：融合法益分析與競合推理的方法
A Legal Cause-Oriented Classification Framework Based on Large Language Models: An Integrated Approach of Legal Interest Analysis and Concurrence Reasoning |
| Authors: | 李旻恆
Lee, Min-Heng |
| Contributors: | 劉昭麟
Liu, Chao-Lin
李旻恆
Lee, Min-Heng |
| Keywords: | 法益分析
案由分類
大型語言模型
提示工程
多案由推理
法律資訊檢索
Legal Interest Analysis
Cause of Action Classification
Large Language Models
Prompt Engineering
Multi-cause Reasoning
Legal Information Retrieval |
| Date: | 2025 |
| Issue Date: | 2025-05-02 15:05:07 (UTC+8) |
| Abstract: | 本研究提出一套結合法益分析與競合推理的法律案由分類框架（LCPF）， 應用大型語言模型（如GPT-4o）解決多案由刑事案件分類中的解釋性與準確性 挑戰。研究針對檢察官起訴書中常見的「傷害」與「竊盜」案由，建立起一套 結構化提示方法，將法益識別、法條競合分析與刑罰輕重比較納入推理流程。 透過單輪與多輪提示策略的設計，模型能更清楚辨識多重法益並正確分類主案 由。在800 筆實驗資料中，LCPF 於多輪提示下可將準確率提升至97-98%，並 在法益分析任務中展現出更高的召回率與F1分數。研究亦探討其在RAG 架構 中的擴展應用與實際系統實作，並邀請專家進行評估。結果證明LCPF 在法律 資訊處理領域具有高度可行性與實務應用潛力，為未來法律AI發展提供關鍵支撐。
This study proposes a Legal Cause-Oriented Prompt Framework (LCPF) that integrates legal interest analysis and concurrence reasoning to address the challenges of explainability and accuracy in multi-cause criminal case classification using Large Language Models (LLMs) such as GPT-4o. Focusing on common indictment cases involving "injury" and "theft," the framework utilizes structured prompting strategies to guide the reasoning process through legal interest identification, legal concurrence analysis, and penalty severity comparison. By designing both single-step and multi-step prompting methods, the model is able to more precisely recognize overlapping legal interests and accurately classify the primary charge. In experiments on 800 samples, LCPF achieved accuracy rates of up to 97–98% with multi-step prompts, and demonstrated superior recall and F1 scores in legal interest recognition tasks. The study further explores the extension of LCPF into Retrieval-Augmented Generation (RAG) systems and presents an implemented legal chatbot evaluated by domain experts. Results show that LCPF is both practical and effective in processing legal information, offering a promising direction for future legal AI applications by enhancing both interpretability and reliability in complex legal scenarios. |
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| Description: | 碩士
國立政治大學
資訊科學系
112753118 |
| Source URI: | http://thesis.lib.nccu.edu.tw/record/#G0112753118 |
| Data Type: | thesis |
| Appears in Collections: | [資訊科學系] 學位論文
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