DARE-RAG: Difficulty-Aware Retrieval Expansion for Retrieval-Augmented Generation

Abstract

Retrieval-Augmented Generation (RAG) systems face a fundamental trade-off: query expansion can improve retrieval effectiveness for ambiguous or underspecified queries, yet indiscriminate expansion introduces unnecessary latency and retrieval noise. Existing RAG pipelines typically apply expansion uniformly, failing to distinguish between easy and retrieval-challenging queries.To address this issue, we propose DARE-RAG, an adaptive retrieval framework that activates LLM-based query expansion only for retrieval-challenging queries. Our method formulates expansion activation as a lightweight binary classification problem using probe retrieval signals, including score margin, variance, entropy, query length, and lexical specificity. A lightweight MLP predicts whether expansion is likely to improve retrieval quality, and expansion is triggered only when the predicted confidence exceeds a percentile-calibrated threshold.DARE-RAG further integrates a dual-path hybrid retrieval architecture combining BM25 sparse retrieval and BGE dense retrieval, fused via Reciprocal Rank Fusion (RRF), followed by a Cross-Encoder reranker for context refinement. Experiments on NQ-Open and HotpotQA demonstrate that DARE-RAG consistently improves retrieval effectiveness and end-to-end QA accuracy while clearly reducing average end-to-end latency compared with corresponding always-expand variants of BM25, BGE-m3, and their RRF-fused hybrid retriever. Extensive ablation studies and efficiency analyses verify the effectiveness of our utility-guided expansion strategy.