用半硬负例优化越南法律文档检索，轻量高效且效果顶尖。
Optimizing Legal Document Retrieval in Vietnamese with Semi-Hard Negative Mining
- 分两阶段：双编码器快速筛选，交叉编码器精准重排。
- 引入新指标Exist@m，半硬负例使重排性能显著提升。
- 轻量单阶段方案媲美大模型，适合资源有限场景。
大型语言模型在法律等专业领域面临精度与领域知识不足的挑战。本文提出一种简化的两阶段框架：先用微调的双编码器快速检索候选文档，再通过交叉编码器进行精确重排，两者均通过策略性负样本挖掘优化。关键创新包括引入新评估指标Exist@m，以及使用半硬负例缓解训练偏差，显著提升重排效果。在2024年SoICT黑客松法律文档检索任务中，本团队4Huiter取得前三名。尽管顶尖队伍采用集成模型与大规模bge-m3架构的迭代自训练，我们的轻量级单通路方法仍以极少参数实现竞争力。结果表明，优化数据处理、定制损失函数与平衡负样本采样对构建鲁棒法律检索系统至关重要。
原文摘要 · Abstract (English)
Large Language Models (LLMs) face significant challenges in specialized domains like law, where precision and domain-specific knowledge are critical. This paper presents a streamlined two-stage framework consisting of Retrieval and Re-ranking to enhance legal document retrieval efficiency and accuracy. Our approach employs a fine-tuned Bi-Encoder for rapid candidate retrieval, followed by a Cross-Encoder for precise re-ranking, both optimized through strategic negative example mining. Key innovations include the introduction of the Exist@m metric to evaluate retrieval effectiveness and the use of semi-hard negatives to mitigate training bias, which significantly improved re-ranking performance. Evaluated on the SoICT Hackathon 2024 for Legal Document Retrieval, our team, 4Huiter, achieved a top-three position. While top-performing teams employed ensemble models and iterative self-training on large bge-m3 architectures, our lightweight, single-pass approach offered a competitive alternative with far fewer parameters. The framework demonstrates that optimized data processing, tailored loss functions, and balanced negative sampling are pivotal for building robust retrieval-augmented systems in legal contexts.
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