@inproceedings{han-etal-2025-reasoning,
title = "Reasoning with Graphs: Structuring Implicit Knowledge to Enhance {LLM}s Reasoning",
author = "Han, Haoyu and
Xie, Yaochen and
Liu, Hui and
Tang, Xianfeng and
Nag, Sreyashi and
Headden, William and
Li, Yang and
Luo, Chen and
Ji, Shuiwang and
He, Qi and
Tang, Jiliang",
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.1319/",
doi = "10.18653/v1/2025.findings-acl.1319",
pages = "25698--25714",
ISBN = "979-8-89176-256-5",
abstract = "Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks; however, they still encounter challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences. This challenge is particularly pronounced in tasks involving multi-step processes, such as logical reasoning and multi-hop question answering, where understanding implicit relationships between entities and leveraging multi-hop connections in the given context are crucial. Graphs, as fundamental data structures, explicitly represent pairwise relationships between entities, thereby offering the potential to enhance LLMs' reasoning capabilities. External graphs have proven effective in supporting LLMs across multiple tasks. However, in many reasoning tasks, no pre-existing graph structure is provided. Can we structure implicit knowledge derived from context into graphs to assist LLMs in reasoning? In this paper, we propose Reasoning with Graphs (RwG) by first constructing explicit graphs from the context and then leveraging these graphs to enhance LLM reasoning performance on reasoning tasks. Extensive experiments demonstrate the effectiveness of the proposed method in improving both logical reasoning and multi-hop question answering tasks."
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<abstract>Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks; however, they still encounter challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences. This challenge is particularly pronounced in tasks involving multi-step processes, such as logical reasoning and multi-hop question answering, where understanding implicit relationships between entities and leveraging multi-hop connections in the given context are crucial. Graphs, as fundamental data structures, explicitly represent pairwise relationships between entities, thereby offering the potential to enhance LLMs’ reasoning capabilities. External graphs have proven effective in supporting LLMs across multiple tasks. However, in many reasoning tasks, no pre-existing graph structure is provided. Can we structure implicit knowledge derived from context into graphs to assist LLMs in reasoning? In this paper, we propose Reasoning with Graphs (RwG) by first constructing explicit graphs from the context and then leveraging these graphs to enhance LLM reasoning performance on reasoning tasks. Extensive experiments demonstrate the effectiveness of the proposed method in improving both logical reasoning and multi-hop question answering tasks.</abstract>
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%0 Conference Proceedings
%T Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning
%A Han, Haoyu
%A Xie, Yaochen
%A Liu, Hui
%A Tang, Xianfeng
%A Nag, Sreyashi
%A Headden, William
%A Li, Yang
%A Luo, Chen
%A Ji, Shuiwang
%A He, Qi
%A Tang, Jiliang
%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 han-etal-2025-reasoning
%X Large language models (LLMs) have demonstrated remarkable success across a wide range of tasks; however, they still encounter challenges in reasoning tasks that require understanding and inferring relationships between distinct pieces of information within text sequences. This challenge is particularly pronounced in tasks involving multi-step processes, such as logical reasoning and multi-hop question answering, where understanding implicit relationships between entities and leveraging multi-hop connections in the given context are crucial. Graphs, as fundamental data structures, explicitly represent pairwise relationships between entities, thereby offering the potential to enhance LLMs’ reasoning capabilities. External graphs have proven effective in supporting LLMs across multiple tasks. However, in many reasoning tasks, no pre-existing graph structure is provided. Can we structure implicit knowledge derived from context into graphs to assist LLMs in reasoning? In this paper, we propose Reasoning with Graphs (RwG) by first constructing explicit graphs from the context and then leveraging these graphs to enhance LLM reasoning performance on reasoning tasks. Extensive experiments demonstrate the effectiveness of the proposed method in improving both logical reasoning and multi-hop question answering tasks.
%R 10.18653/v1/2025.findings-acl.1319
%U https://aclanthology.org/2025.findings-acl.1319/
%U https://doi.org/10.18653/v1/2025.findings-acl.1319
%P 25698-25714
Markdown (Informal)
[Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning](https://aclanthology.org/2025.findings-acl.1319/) (Han et al., Findings 2025)
ACL
- Haoyu Han, Yaochen Xie, Hui Liu, Xianfeng Tang, Sreyashi Nag, William Headden, Yang Li, Chen Luo, Shuiwang Ji, Qi He, and Jiliang Tang. 2025. Reasoning with Graphs: Structuring Implicit Knowledge to Enhance LLMs Reasoning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 25698–25714, Vienna, Austria. Association for Computational Linguistics.