Adaptive Retrieval-Augmented Generation aims to mitigate the interference of extraneous noise by dynamically determining the necessity of retrieving supplementary passages. However, as Large Language Models evolve with increasing robustness to noise, the necessity of adaptive retrieval warrants re-evaluation. In this paper, we rethink this necessity and propose AdaRankLLM, a novel adaptive retrieval framework. To effectively verify the necessity of adaptive listwise reranking, we first develop an adaptive ranker employing a zero-shot prompt with a passage dropout mechanism, and compare its generation outcomes against static fixed-depth retrieval strategies. Furthermore, to endow smaller open-source LLMs with this precise listwise ranking and adaptive filtering capability, we introduce a two-stage progressive distillation paradigm enhanced by data sampling and augmentation techniques. Extensive experiments across three datasets and eight LLMs demonstrate that AdaRankLLM consistently achieves optimal performance in most scenarios with significantly reduced context overhead. Crucially, our analysis reveals a role shift in adaptive retrieval: it functions as a critical noise filter for weaker models to overcome their limitations, while serving as a cost-effective efficiency optimizer for stronger reasoning models.

翻译：自适应检索增强生成旨在通过动态判断是否需要补充检索段落，来减少无关噪声的干扰。然而，随着大语言模型对噪声鲁棒性的不断增强，自适应检索的必要性需要重新评估。本文重新审视了这一必要性，并提出了一种新型自适应检索框架AdaRankLLM。为有效验证自适应列表重排序的必要性，我们首先构建了一个采用零样本提示和段落丢弃机制的自适应排序器，并将其生成结果与静态固定深度检索策略进行对比。此外，为使小型开源大语言模型具备精确的列表排序和自适应过滤能力，我们引入了一种结合数据采样与增强技术的两阶段渐进式蒸馏范式。在三个数据集和八种大语言模型上的大量实验表明，AdaRankLLM在多数场景下能以显著降低的上下文开销实现最优性能。关键的是，我们的分析揭示了自适应检索的作用转变：对较弱模型而言，它是克服局限性的关键噪声过滤器；而对较强推理模型来说，它则成为提升效率的经济型优化器。