企业知识检索的系统框架：利用LLM生成的元数据增强RAG系统
A Systematic Framework for Enterprise Knowledge Retrieval: Leveraging LLM-Generated Metadata to Enhance RAG Systems
- University of Illinois Chicago（伊利诺伊大学芝加哥分校）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文提出一种系统性的元数据增强框架，利用大语言模型提升RAG系统的文档检索性能，通过实验验证元数据增强对检索精度和排名质量的提升效果。
AI中文摘要：
在企业环境中，从大规模复杂知识库中高效检索相关信息对于运营生产力和明智决策至关重要。本文提出一种系统性的经验框架，利用大语言模型（LLMs）对元数据进行增强，以提升RAG系统中的文档检索能力。我们的方法采用一个结构化的流水线，动态生成文档片段的有意义元数据，显著改善其语义表示和检索准确性。通过一个受控的3×3实验矩阵，我们比较了三种分块策略——语义、递归和朴素——以及三种嵌入技术——内容-only、TF-IDF加权和前缀融合——并通过消融分析隔离每个组件的贡献。结果表明，元数据增强的方法在精度和排名质量上均优于内容-only基线，其中递归分块与TF-IDF加权嵌入组合达到82.5%的精度，而朴素分块与前缀融合组合在排名质量上表现最佳（NDCG 0.813）。我们的评估采用交叉编码器重排序生成银标准真实值，统计显著性通过Bonferroni校正配对t检验确认。这些发现证实了元数据增强能够提高向量空间组织和检索效果，同时保持低于30毫秒的P95延迟，为在企业环境中部署高性能、可扩展的RAG系统提供了量化决策框架。
英文摘要：
In enterprise settings, efficiently retrieving relevant information from large and complex knowledge bases is essential for operational productivity and informed decision-making. This research presents a systematic empirical framework for metadata enrichment using large language models (LLMs) to enhance document retrieval in Retrieval-Augmented Generation (RAG) systems. Our approach employs a structured pipeline that dynamically generates meaningful metadata for document segments, substantially improving their semantic representations and retrieval accuracy. Through a controlled 3 X 3 experimental matrix, we compare three chunking strategies -- semantic, recursive, and naive -- and evaluate their interactions with three embedding techniques -- content-only, TF-IDF weighted, and prefix-fusion -- isolating the contribution of each component through ablation analysis. The results demonstrate that metadata-enriched approaches consistently outperform content-only baselines, with recursive chunking paired with TF-IDF weighted embeddings yielding 82.5% precision and naive chunking with prefix-fusion achieving the strongest ranking quality (NDCG 0.813). Our evaluation employs cross-encoder reranking for silver-standard ground truth generation, with statistical significance confirmed via Bonferroni-corrected paired t-tests. These findings confirm that metadata enrichment improves vector space organization and retrieval effectiveness while maintaining sub-30 ms P95 latency, providing a quantitative decision framework for deploying high-performance, scalable RAG systems in enterprise settings.
