IterKey：利用大语言模型迭代生成关键词以增强检索增强生成
IterKey: Iterative Keyword Generation with LLMs for Enhanced Retrieval Augmented Generation
AI总结：
针对现有RAG方法难以兼顾准确性与可解释性的问题，提出由LLM驱动的三阶段迭代关键词生成框架IterKey，通过稀疏检索增强RAG，在四项QA任务上准确率较BM25基线提升5%-20%，性能媲美密集检索类方法，实现了准确性与可解释性的平衡。
摘要：
检索增强生成（Retrieval-Augmented Generation，RAG）已成为一种通过整合外部文档来补充大语言模型（Large Language Models，LLMs）上下文内知识的方法。然而，实际应用不仅要求准确性，还要求可解释性。密集检索方法虽能提供高准确性，但缺乏可解释性；相反，稀疏检索方法具备透明度，但由于依赖关键词匹配，往往无法捕捉查询的完整意图。为解决这些问题，我们提出IterKey——一种由LLM驱动的迭代关键词生成框架，通过稀疏检索增强RAG性能。IterKey包含三个由LLM驱动的阶段：生成检索用关键词、基于检索到的文档生成答案、验证答案。若验证失败，流程将使用优化后的关键词迭代重复。在四项问答（QA）任务上的实验结果表明，IterKey相比基于BM25的RAG和简单基线，实现了5%至20%的准确率提升。其性能与基于密集检索的RAG以及现有使用密集模型的迭代查询优化方法相当。综上，IterKey是一种新颖的基于BM25的方法，它利用LLMs迭代优化RAG，有效平衡了准确性与可解释性。
英文摘要：
Retrieval-Augmented Generation (RAG) has emerged as a way to complement the in-context knowledge of Large Language Models (LLMs) by integrating external documents. However, real-world applications demand not only accuracy but also interpretability. While dense retrieval methods provide high accuracy, they lack interpretability; conversely, sparse retrieval methods offer transparency but often fail to capture the full intent of queries due to their reliance on keyword matching. To address these issues, we introduce IterKey, an LLM-driven iterative keyword generation framework that enhances RAG via sparse retrieval. IterKey consists of three LLM-driven stages: generating keywords for retrieval, generating answers based on retrieved documents, and validating the answers. If validation fails, the process iteratively repeats with refined keywords. Across four QA tasks, experimental results show that IterKey achieves 5% to 20% accuracy improvements over BM25-based RAG and simple baselines. Its performance is comparable to dense retrieval-based RAG and prior iterative query refinement methods using dense models. In summary, IterKey is a novel BM25-based approach leveraging LLMs to iteratively refine RAG, effectively balancing accuracy with interpretability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
