按法律条文结构切块，能更好回答德国法律问题。
Chunking German Legal Code
- 用法律条文本身结构（如章节、小节）做分块，效果最好。
- 按条文结构分块的召回率最高，且速度更快、占用资源少。
- 适合需要高效准确检索法律文本的研究者和开发者。
本文研究在德国民法典上进行检索增强生成时的分块策略，以德国民法典为结构化基准语料。实验对比了多种分块方法：结构单元（条、款、句、段）、固定大小窗口、上下文分块、语义聚类、Lumber风格分块及RAPTOR式分层检索。所有方法在带条级黄金标签的法律问答数据集上评估，指标包括召回率、查询延迟、索引构建时间和存储开销。结果显示，与法律内在结构一致的分块（特别是条、款级别）取得最高召回率，而强行打破结构的复杂方法表现更差。这些简单方法在计算效率上显著优于依赖大模型的上下文分块、RAPTOR和Lumber方法。研究揭示了语义丰富性与运行成本间的权衡，并表明保留领域特定结构对法律信息检索至关重要。
原文摘要 · Abstract (English)
This paper investigates chunking strategies for retrieval-augmented generation on German statutory law, using the German Civil Code as a structured benchmark corpus. We implement and compare a range of segmentation approaches, including structural units (sections, subsections, sentences, propositions), fixed-size windows, contextual chunking, semantic clustering, Lumber-style chunking, and RAPTOR-based hierarchical retrieval. All methods are evaluated on a legal question-answering dataset with section-level gold labels, measuring recall, query latency, index build time, and storage requirements. Results show that chunking strategies aligned with the inherent legal structure - particularly section and subsection - based retrieval-achieve the highest recall, while more complex approaches that override this structure perform worse. These simpler methods also offer favorable computational efficiency compared to LLM-intensive techniques such as contextual chunking, RAPTOR, and Lumber. The findings highlight a key trade-off between semantic enrichment and operational cost, and demonstrate that preserving domain-specific structure is critical for effective legal information retrieval.
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