@inproceedings{zhou-etal-2025-reflection,
title = "Reflection on Knowledge Graph for Large Language Models Reasoning",
author = "Zhou, Yigeng and
Li, Wu and
Lu, Yifan and
Li, Jing and
Liu, Fangming and
Zhang, Meishan and
Wang, Yequan and
He, Daojing and
Liu, Honghai and
Zhang, Min",
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.1221/",
doi = "10.18653/v1/2025.findings-acl.1221",
pages = "23840--23857",
ISBN = "979-8-89176-256-5",
abstract = "Recent research shows that supplementing Large Language Models (LLMs) with knowledge graphs can enhance their performance. However, existing methods often introduce noise in the retrieval and reasoning pipeline, hindering LLMs' ability to effectively integrate external knowledge for complex multi-hop question answering. To address this, we propose RefKG, a novel framework designed to enhance the reasoning capabilities of LLMs through reflective engagement with knowledge graphs. RefKG autonomously conduct retrieval and reflection on knowledge graphs. It consists of three modules: Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. We also introduce a multi-task tuning strategy that not only integrates external knowledge into LLMs but also trains them to leverage this knowledge for answering questions. This significantly improves their performance on knowledge-intensive tasks. Experiments on fact verification and knowledge graph question answering demonstrate RefKG{'}s effectiveness."
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<abstract>Recent research shows that supplementing Large Language Models (LLMs) with knowledge graphs can enhance their performance. However, existing methods often introduce noise in the retrieval and reasoning pipeline, hindering LLMs’ ability to effectively integrate external knowledge for complex multi-hop question answering. To address this, we propose RefKG, a novel framework designed to enhance the reasoning capabilities of LLMs through reflective engagement with knowledge graphs. RefKG autonomously conduct retrieval and reflection on knowledge graphs. It consists of three modules: Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. We also introduce a multi-task tuning strategy that not only integrates external knowledge into LLMs but also trains them to leverage this knowledge for answering questions. This significantly improves their performance on knowledge-intensive tasks. Experiments on fact verification and knowledge graph question answering demonstrate RefKG’s effectiveness.</abstract>
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%0 Conference Proceedings
%T Reflection on Knowledge Graph for Large Language Models Reasoning
%A Zhou, Yigeng
%A Li, Wu
%A Lu, Yifan
%A Li, Jing
%A Liu, Fangming
%A Zhang, Meishan
%A Wang, Yequan
%A He, Daojing
%A Liu, Honghai
%A Zhang, Min
%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 zhou-etal-2025-reflection
%X Recent research shows that supplementing Large Language Models (LLMs) with knowledge graphs can enhance their performance. However, existing methods often introduce noise in the retrieval and reasoning pipeline, hindering LLMs’ ability to effectively integrate external knowledge for complex multi-hop question answering. To address this, we propose RefKG, a novel framework designed to enhance the reasoning capabilities of LLMs through reflective engagement with knowledge graphs. RefKG autonomously conduct retrieval and reflection on knowledge graphs. It consists of three modules: Query Decoupling, LLM-Driven Knowledge Graph Exploration, and Inference with Knowledge Reconstruction. We also introduce a multi-task tuning strategy that not only integrates external knowledge into LLMs but also trains them to leverage this knowledge for answering questions. This significantly improves their performance on knowledge-intensive tasks. Experiments on fact verification and knowledge graph question answering demonstrate RefKG’s effectiveness.
%R 10.18653/v1/2025.findings-acl.1221
%U https://aclanthology.org/2025.findings-acl.1221/
%U https://doi.org/10.18653/v1/2025.findings-acl.1221
%P 23840-23857
Markdown (Informal)
[Reflection on Knowledge Graph for Large Language Models Reasoning](https://aclanthology.org/2025.findings-acl.1221/) (Zhou et al., Findings 2025)
ACL
- Yigeng Zhou, Wu Li, Yifan Lu, Jing Li, Fangming Liu, Meishan Zhang, Yequan Wang, Daojing He, Honghai Liu, and Min Zhang. 2025. Reflection on Knowledge Graph for Large Language Models Reasoning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 23840–23857, Vienna, Austria. Association for Computational Linguistics.