Rethinking the Necessity of Adaptive Retrieval-Augmented Generation through the Lens of Adaptive Listwise Ranking

2026-04-16

AI Summary

As large language models (LLMs) become increasingly robust to noise, the conventional dynamic “retrieve-or-not” decision mechanism in adaptive retrieval-augmented generation warrants reevaluation. This work proposes AdaRankLLM, a framework that integrates zero-shot prompting with a paragraph-dropping strategy to construct an adaptive ranker, and employs a two-stage progressive distillation process to transfer precise list-wise ranking and adaptive filtering capabilities to smaller open-source LLMs. The study reveals that adaptive retrieval primarily serves as a critical noise filter for weaker models, while for stronger models it mainly reduces inference cost; it further provides the first empirical validation of the necessity of adaptive list-wise ranking. Evaluated across three datasets and eight LLMs, AdaRankLLM substantially outperforms fixed-depth retrieval strategies, achieving state-of-the-art performance in most settings while significantly lowering context overhead.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Adaptive Retrieval-Augmented Generation
• noise robustness
• listwise ranking
• retrieval necessity
• large language models

Innovation

• Adaptive Retrieval-Augmented Generation
• Listwise Ranking
• Progressive Distillation
• Noise Filtering
• Zero-shot Prompting