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.

本研究解决了现有自适应检索方法依赖大语言模型而导致的效率低下和实用性不强的问题。我们提出了一种基于外部信息的轻量级自适应检索方法，并在多个问答数据集上进行评估，结果显示该方法在保持与复杂模型相当的性能的同时，实现了显著的效率提升，展现了外部信息在自适应检索中的潜力。

独立于大语言模型的自适应检索增强生成：让问题本身发声