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Expand, Rerank, and Retrieve: Query Reranking for Open-Domain Question Answering

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arxiv 2305.17080 v1 pith:7TDEHZL7 submitted 2023-05-26 cs.CL

Yung-Sung Chuang , Wei Fang , Shang-Wen Li , Wen-tau Yih , James Glass This is my paper
classification cs.CL
keywords queryexpansionmodelretrievalansweringbetteropen-domainqueries
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We propose EAR, a query Expansion And Reranking approach for improving passage retrieval, with the application to open-domain question answering. EAR first applies a query expansion model to generate a diverse set of queries, and then uses a query reranker to select the ones that could lead to better retrieval results. Motivated by the observation that the best query expansion often is not picked by greedy decoding, EAR trains its reranker to predict the rank orders of the gold passages when issuing the expanded queries to a given retriever. By connecting better the query expansion model and retriever, EAR significantly enhances a traditional sparse retrieval method, BM25. Empirically, EAR improves top-5/20 accuracy by 3-8 and 5-10 points in in-domain and out-of-domain settings, respectively, when compared to a vanilla query expansion model, GAR, and a dense retrieval model, DPR.

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Cited by 3 Pith papers

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

  1. Aligned Query Expansion: Efficient Query Expansion for Information Retrieval through LLM Alignment

    cs.IR 2025-07 conditional novelty 6.0 of 10

    AQE uses retrieval rank as a preference signal to fine-tune T0 with RSFT and DPO, beating generate-then-filter baselines on four QA datasets.

  2. SLMs as Multi-Agent Routers: A Progressive SFT and Reinforcement Learning Approach

    cs.CL 2026-07 reject novelty 5.0 of 10

    A 0.6B router trained by SFT+RL on retrieval-quality rewards reaches 0.771 NDCG@10 across 11 agents, beating intent-prompted LLMs and cutting latency by 82%.

  3. Open-Source Agentic Hybrid RAG Framework for Scientific Literature Review

    cs.IR 2025-07 conditional novelty 4.0 of 10

    A DPO-tuned agentic hybrid RAG system that routes queries between a knowledge graph and a vector store beat a static baseline on a self-generated benchmark.

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