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Generate-then-Ground in Retrieval-Augmented Generation for Multi-hop Question Answering

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arxiv 2406.14891 v2 pith:PLHXV2EC submitted 2024-06-21 cs.CL cs.IR

Zhengliang Shi , Weiwei Sun , Shen Gao , Pengjie Ren , Zhumin Chen , Zhaochun Ren This is my paper
classification cs.CLcs.IR
keywords answerdocumentsquestionllmsmethodmulti-hopansweringexternal
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Multi-Hop Question Answering (MHQA) tasks present a significant challenge for large language models (LLMs) due to the intensive knowledge required. Current solutions, like Retrieval-Augmented Generation, typically retrieve potential documents from an external corpus to read an answer. However, the performance of this retrieve-then-read paradigm is constrained by the retriever and the inevitable noise in the retrieved documents. To mitigate these challenges, we introduce a novel generate-then-ground (GenGround) framework, synergizing the parametric knowledge of LLMs and external documents to solve a multi-hop question. GenGround empowers LLMs to alternate two phases until the final answer is derived: (1) formulate a simpler, single-hop question and directly generate the answer; (2) ground the question-answer pair in retrieved documents, amending any wrong predictions in the answer. We also propose an instructional grounding distillation method to generalize our method into smaller models. Extensive experiments conducted on four datasets illustrate the superiority of our method.

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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. PRGB Benchmark: A Robust Placeholder-Assisted Algorithm for Benchmarking Retrieval-Augmented Generation

    cs.CL 2025-07 conditional novelty 6.0 of 10

    PRGB introduces a placeholder-based, fine-grained RAG benchmark that evaluates LLMs on filtering, combination, and multi-hop reasoning, with English and Chinese datasets.

  2. DeepShop: A Benchmark for Deep Research Shopping Agents

    cs.IR 2025-06 conditional novelty 6.0 of 10

    DeepShop, a benchmark of 150 complex online shopping queries with fine-grained evaluation, shows that leading web agents and deep research systems achieve at most a 32% task success rate.

  3. A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models

    cs.IR 2025-07 conditional novelty 5.0 of 10

    QMKGF builds multi-path knowledge graph subgraphs from LLM-extracted entities, fuses the highest-scoring subgraph with query-relevant triples, and expands the query to improve RAG answer quality.

  4. Iterative Self-Incentivization Empowers Large Language Models as Agentic Searchers

    cs.CL 2025-05 conditional novelty 5.0 of 10

    EXSEARCH trains LLMs for agentic search by treating search trajectories as latent variables and optimizing a weighted likelihood via expectation-maximization, yielding gains on NQ, HotpotQA, MuSiQue, and 2WikiQA.

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