This title slide presents "Research and Implementation of Methods for Legal Case Retrieval" as the main topic. The subtitle labels it a "Thesis Proposal Report" in "Blue-White Technology Style."

法律案例检索的方法研究与实现

开题报告 | 蓝白科技风

Source: 开题报告

This section header slide, titled "Research Background and Significance" (Section 01), introduces the first part of a presentation on legal case retrieval methods research and implementation. Its subtitle outlines coverage of legal case retrieval's importance, traditional methods' limitations, and the research's significance.

法律案例检索的方法研究与实现

01

课题背景与意义

阐述法律案例检索的重要性、传统方法局限性及研究意义

Source: 开题报告

The slide "国内外研究现状" outlines retrieval research progression: sparse BM25 (keyword matching with low recall), dense retrieval (BERT vector similarity), and generative retrieval (SAILER directly generates documents), each with diagrams. It highlights the latest advance in Ultron end-to-end retrieval, also with a diagram.

国内外研究现状

• 稀疏检索（BM25）：关键词精确匹配，召回率低
• 稠密检索：向量相似度，BERT模型结构[示意图]
• 生成式检索：直接生成文档，SAILER结构[示意图]
• 最新进展：Ultron端到端检索[示意图]

Source: 《法律案例检索的方法研究与实现》开题报告

The slide compares retrieval methods: sparse (BM25) for fast efficiency ignoring semantics, dense (BERT/SAILER) for deep semantic capture at high cost, and generative (Ultron) for end-to-end generation with complex training. It highlights the trend evolving from dense to generative approaches, illustrated by model schematics.

检索方法对比与模型示意图

!Image

• 稀疏检索(BM25)：高效快速，忽略语义相似性
• 稠密检索(BERT/SAILER)：捕捉深层语义，高计算成本
• 生成式检索(Ultron)：端到端生成，训练复杂
• 发展趋势：稠密向生成式演进

Source: Image from Wikipedia article "Transformer (deep learning)"

This slide serves as the section header for Section 03, titled "Research Content and Proposed Solutions." The subtitle highlights combining information extraction with generative retrieval, optimizing codeword design, contrastive learning research, and building an end-to-end system.

研究内容与拟采取的方案

03

研究内容与拟采取的方案

信息抽取与生成式检索结合、码字设计优化、对比学习研究，构建端到端系统

Source: 《法律案例检索的方法研究与实现》开题报告

The slide outlines a four-phase workflow for the proposed technical solution in legal case processing: case information extraction to build structured inputs, token optimization encoding to improve vector quality, contrastive learning training to enhance model discrimination, and generative retrieval output to boost accuracy. Each phase specifies core activities and targeted goals for efficient, precise case retrieval.

拟采取的技术方案

{ "headers": [ "阶段", "核心内容", "目标" ], "rows": [ [ "案例信息抽取", "从法律案例中提取关键信息，如事实、法律条款等", "构建高质量结构化输入" ], [ "码字优化编码", "设计优化码字方案，提升编码效率和语义表示", "改善向量表示质量" ], [ "对比学习训练", "采用对比学习方法训练模型，拉近正样本距离", "增强模型区分能力" ], [ "生成式检索输出", "基于生成式检索直接输出相关案例ID", "提升检索精准度" ] ] }

Source: 根据《法律案例检索的方法研究与实现》开题报告内容

The slide identifies end-to-end modeling as the key technology, with main challenges being low retrieval accuracy and poor model generalization across legal domains. It proposes solving these via self-supervised learning combined with contrastive learning.

关键技术与难点

• • 关键技术：端到端建模
• • 难点1：检索准确率低
• • 难点2：模型泛化性差（跨领域法律文本）
• • 拟解决：自监督 + 对比学习

Source: 《法律案例检索的方法研究与实现》开题报告

The slide outlines a project timeline from November 2025 to June 2027 for developing a generative legal case retrieval model. It includes three phases: literature review and model design (Nov 2025-Mar 2026), system implementation and experiments (Apr-Dec 2026), and optimization, testing, plus paper writing (Jan-Jun 2027).

进度安排

2025.11-2026.03: 文献调研与模型设计 全面调研法律案例检索相关文献，完成生成式检索模型初步设计方案。 2026.04-2026.12: 系统实现与实验 开发系统原型，进行大规模实验验证模型在检索准确率和效率上的性能。 2027.01-2027.06: 优化测试与论文撰写 针对难点进行模型优化、全面测试，并完成开题报告与论文初稿撰写。

This slide, titled "参考文献" (References), features a table listing key papers alongside their authors and publication years. It includes BM25 (Robertson, 1994), BERT (Devlin, 2019), DSI (Guu, 2020), and Ultron ([latest]).

参考文献

{ "headers": [ "论文", "作者/年份" ], "rows": [ [ "BM25", "Robertson, 1994" ], [ "BERT", "Devlin, 2019" ], [ "DSI", "Guu, 2020" ], [ "Ultron", "[最新]" ] ] }

The slide, titled "致谢" (Acknowledgments), thanks the supervisor for guidance and reviewers for their valuable opinions. It expresses anticipation for further exchange and corrections, ending with thanks for listening.

致谢

感谢导师指导与评审老师宝贵意见！🙏

期待交流与指正。谢谢聆听！

Source: 《法律案例检索的方法研究与实现》开题报告