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Bridging the Preference Gap between Retrievers and LLMs

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arxiv 2401.06954 v2 pith:M3RHN3XY submitted 2024-01-13 cs.CL

Zixuan Ke , Weize Kong , Cheng Li , Mingyang Zhang , Qiaozhu Mei , Michael Bendersky This is my paper · ORCID
classification cs.CL
keywords contextllmsretrieversbridgefriendlygenerationinformationresults
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Large Language Models (LLMs) have demonstrated superior results across a wide range of tasks, and Retrieval-augmented Generation (RAG) is an effective way to enhance the performance by locating relevant information and placing it into the context window of the LLM. However, the relationship between retrievers and LLMs in a RAG is still under-investigated. Most existing work treats the retriever and the LLM as independent components and leaves a gap between retrieving human-"friendly" information and assembling a LLM-"friendly" context. In this work, we examine a novel bridge mechanism. We validate the ranking and selection assumptions of retrievers in the context of RAG and propose a framework that chains together supervised and reinforcement learning to train a bridge model that optimizes the connection between the retriever and the LLM. Empirical results demonstrate the effectiveness of our method in both question-answering and personalized generation tasks.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GainRAG: Preference Alignment in Retrieval-Augmented Generation through Gain Signal Synthesis

    cs.IR 2025-05 conditional novelty 6.0 of 10

    GainRAG aligns retriever and LLM preferences by training a selector on contrastive-perplexity 'gain' signals plus a pseudo-passage fallback, improving RAG accuracy on six QA datasets.

  2. ARAG: Agentic Retrieval Augmented Generation for Personalized Recommendation

    cs.IR 2025-06 conditional novelty 5.0 of 10

    A four-agent LLM pipeline (user understanding, natural language inference, context summarization, and ranking) improves retrieval-augmented product recommendations on Amazon data by up to 42% in NDCG@5 over recency an...

  3. RADIANT: Retrieval AugmenteD entIty-context AligNmenT -- Introducing RAG-ability and Entity-Context Divergence

    cs.CL 2025-06 reject novelty 4.0 of 10

    Introduces an entity-context divergence metric and a DPO-based training objective to improve retrieval-augmented generation, with weak empirical validation.

  4. Leveraging LLM-Assisted Query Understanding for Live Retrieval-Augmented Generation

    cs.CL 2025-06 conditional novelty 4.0 of 10

    Omni-RAG, a query-rewriting and decomposition pipeline on top of standard retrieval and reranking, achieved rank 2 in the SIGIR 2025 LiveRAG Challenge.

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