不依赖大模型，用外部信息实现高效自适应检索
LLM-Independent Adaptive RAG: Let the Question Speak for Itself
- 基于外部信息设计轻量级自适应检索机制
- 在6个数据集上达到与复杂方法相当的准确率
- 适合追求高效低耗的实用型AI系统
大语言模型易产生幻觉，检索增强生成（RAG）可缓解此问题，但计算开销大且可能引入错误信息。自适应检索旨在仅在必要时检索，但现有方法依赖大模型进行不确定性评估，效率低下且不实用。本研究提出基于外部信息的轻量级、不依赖大模型的自适应检索方法，考察了27个特征及其7组组合方式。在6个问答数据集上评估了性能与效率。结果表明，该方法在保持与复杂大模型方法相当的问答表现的同时，显著提升效率，验证了外部信息在自适应检索中的潜力。
原文摘要 · Abstract (English)
Large Language Models~(LLMs) are prone to hallucinations, and Retrieval-Augmented Generation (RAG) helps mitigate this, but at a high computational cost while risking misinformation. Adaptive retrieval aims to retrieve only when necessary, but existing approaches rely on LLM-based uncertainty estimation, which remain inefficient and impractical. In this study, we introduce lightweight LLM-independent adaptive retrieval methods based on external information. We investigated 27 features, organized into 7 groups, and their hybrid combinations. We evaluated these methods on 6 QA datasets, assessing the QA performance and efficiency. The results show that our approach matches the performance of complex LLM-based methods while achieving significant efficiency gains, demonstrating the potential of external information for adaptive retrieval.
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