Enhancing Retrieval-Augmented Generation with Two-Stage Retrieval: FlashRank Reranking and Query Expansion

• URL: http://arxiv.org/abs/2601.03258v1
• Date: Fri, 17 Oct 2025 15:08:17 GMT
• Title: Enhancing Retrieval-Augmented Generation with Two-Stage Retrieval: FlashRank Reranking and Query Expansion
• Authors: Sherine George,
• Abstract summary: RAG couples a retriever with a large language model (LLM) to ground generated responses in external evidence.<n>We propose a two-stage retrieval pipeline that integrates LLM-driven query expansion to improve candidate recall.<n>FlashRank is a fast marginal-utility reranker that dynamically selects an optimal subset of evidence under a token budget.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Retrieval-Augmented Generation (RAG) couples a retriever with a large language model (LLM) to ground generated responses in external evidence. While this framework enhances factuality and domain adaptability, it faces a key bottleneck: balancing retrieval recall with limited LLM context. Retrieving too few passages risks missing critical context, while retrieving too many overwhelms the prompt window, diluting relevance and increasing cost. We propose a two-stage retrieval pipeline that integrates LLM-driven query expansion to improve candidate recall and FlashRank, a fast marginal-utility reranker that dynamically selects an optimal subset of evidence under a token budget. FlashRank models document utility as a weighted combination of relevance, novelty, brevity, and cross-encoder evidence. Together, these modules form a generalizable solution that increases answer accuracy, faithfulness, and computational efficiency.

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