@inproceedings{ji-etal-2025-resource,
title = "Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering",
author = "Ji, Binquan and
Luo, Haibo and
YifeiLu, YifeiLu and
Hei, Lei and
Wang, Jiaqi and
Liao, Tingjing and
Lingyu, Wang and
Wang, Shichao and
Ren, Feiliang",
editor = "Che, Wanxiang and
Nabende, Joyce and
Shutova, Ekaterina and
Pilehvar, Mohammad Taher",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2025",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-acl.846/",
doi = "10.18653/v1/2025.findings-acl.846",
pages = "16461--16479",
ISBN = "979-8-89176-256-5",
abstract = "Knowledge-intensive multi-hop question answering (QA) tasks, which require integrating evidence from multiple sources to address complex queries, often necessitate multiple rounds of retrieval and iterative generation by large language models (LLMs). However, incorporating many documents and extended contexts poses challenges{---}such as hallucinations and semantic drift{---}for lightweight LLMs with fewer parameters. This work proposes a novel framework called DEC (Dynamic Enhancement Chain). DEC first decomposes complex questions into logically coherent subquestions to form a hallucination-free reasoning chain. It then iteratively refines these subquestions through context-aware rewriting to generate effective query formulations. For retrieval, we introduce a lightweight discriminative keyword extraction module that leverages extracted keywords to achieve targeted, precise document recall with relatively low computational overhead. Extensive experiments on three multi-hop QA datasets demonstrate that DEC performs on par with or surpasses state-of-the-art benchmarks while significantly reducing token consumption. Notably, our approach attains state-of-the-art results on models with 8B parameters, showcasing its effectiveness in various scenarios, particularly in resource-constrained environments."
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<abstract>Knowledge-intensive multi-hop question answering (QA) tasks, which require integrating evidence from multiple sources to address complex queries, often necessitate multiple rounds of retrieval and iterative generation by large language models (LLMs). However, incorporating many documents and extended contexts poses challenges—such as hallucinations and semantic drift—for lightweight LLMs with fewer parameters. This work proposes a novel framework called DEC (Dynamic Enhancement Chain). DEC first decomposes complex questions into logically coherent subquestions to form a hallucination-free reasoning chain. It then iteratively refines these subquestions through context-aware rewriting to generate effective query formulations. For retrieval, we introduce a lightweight discriminative keyword extraction module that leverages extracted keywords to achieve targeted, precise document recall with relatively low computational overhead. Extensive experiments on three multi-hop QA datasets demonstrate that DEC performs on par with or surpasses state-of-the-art benchmarks while significantly reducing token consumption. Notably, our approach attains state-of-the-art results on models with 8B parameters, showcasing its effectiveness in various scenarios, particularly in resource-constrained environments.</abstract>
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%0 Conference Proceedings
%T Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering
%A Ji, Binquan
%A Luo, Haibo
%A YifeiLu, YifeiLu
%A Hei, Lei
%A Wang, Jiaqi
%A Liao, Tingjing
%A Lingyu, Wang
%A Wang, Shichao
%A Ren, Feiliang
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Findings of the Association for Computational Linguistics: ACL 2025
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-256-5
%F ji-etal-2025-resource
%X Knowledge-intensive multi-hop question answering (QA) tasks, which require integrating evidence from multiple sources to address complex queries, often necessitate multiple rounds of retrieval and iterative generation by large language models (LLMs). However, incorporating many documents and extended contexts poses challenges—such as hallucinations and semantic drift—for lightweight LLMs with fewer parameters. This work proposes a novel framework called DEC (Dynamic Enhancement Chain). DEC first decomposes complex questions into logically coherent subquestions to form a hallucination-free reasoning chain. It then iteratively refines these subquestions through context-aware rewriting to generate effective query formulations. For retrieval, we introduce a lightweight discriminative keyword extraction module that leverages extracted keywords to achieve targeted, precise document recall with relatively low computational overhead. Extensive experiments on three multi-hop QA datasets demonstrate that DEC performs on par with or surpasses state-of-the-art benchmarks while significantly reducing token consumption. Notably, our approach attains state-of-the-art results on models with 8B parameters, showcasing its effectiveness in various scenarios, particularly in resource-constrained environments.
%R 10.18653/v1/2025.findings-acl.846
%U https://aclanthology.org/2025.findings-acl.846/
%U https://doi.org/10.18653/v1/2025.findings-acl.846
%P 16461-16479
Markdown (Informal)
[Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering](https://aclanthology.org/2025.findings-acl.846/) (Ji et al., Findings 2025)
ACL
- Binquan Ji, Haibo Luo, YifeiLu YifeiLu, Lei Hei, Jiaqi Wang, Tingjing Liao, Wang Lingyu, Shichao Wang, and Feiliang Ren. 2025. Resource-Friendly Dynamic Enhancement Chain for Multi-Hop Question Answering. In Findings of the Association for Computational Linguistics: ACL 2025, pages 16461–16479, Vienna, Austria. Association for Computational Linguistics.