adaptive-ranking

This paper proposes AdaRankLLM, an adaptive retrieval framework that challenges the necessity of adaptive RAG by using listwise ranking to dynamically filter retrieved passages. The work shows that adaptive retrieval serves as a noise filter for weaker models while acting as a cost-efficiency optimizer for stronger models, with extensive experiments across multiple datasets and LLMs.