@inproceedings{xu-etal-2025-memory,
title = "Memory-augmented Query Reconstruction for {LLM}-based Knowledge Graph Reasoning",
author = "Xu, Mufan and
Liang, Gewen and
Chen, Kehai and
Wang, Wei and
Zhou, Xun and
Yang, Muyun and
Zhao, Tiejun 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.1234/",
doi = "10.18653/v1/2025.findings-acl.1234",
pages = "24068--24084",
ISBN = "979-8-89176-256-5",
abstract = "Large language models (LLMs) have achieved remarkable performance on knowledge graph question answering (KGQA) tasks by planning and interacting with knowledge graphs. However, existing methods often confuse tool utilization with knowledge reasoning, harming readability of model outputs and giving rise to hallucinatory tool invocations, which hinder the advancement of KGQA. To address this issue, we propose Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (MemQ) to decouple LLM from tool invocation tasks using LLM-built query memory. By establishing a memory module with explicit descriptions of query statements, the proposed MemQ facilitates the KGQA process with natural language reasoning and memory-augmented query reconstruction. Meanwhile, we design an effective and readable reasoning to enhance the LLM{'}s reasoning capability in KGQA. Experimental results that MemQ achieves state-of-the-art performance on widely used benchmarks WebQSP and CWQ."
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<abstract>Large language models (LLMs) have achieved remarkable performance on knowledge graph question answering (KGQA) tasks by planning and interacting with knowledge graphs. However, existing methods often confuse tool utilization with knowledge reasoning, harming readability of model outputs and giving rise to hallucinatory tool invocations, which hinder the advancement of KGQA. To address this issue, we propose Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (MemQ) to decouple LLM from tool invocation tasks using LLM-built query memory. By establishing a memory module with explicit descriptions of query statements, the proposed MemQ facilitates the KGQA process with natural language reasoning and memory-augmented query reconstruction. Meanwhile, we design an effective and readable reasoning to enhance the LLM’s reasoning capability in KGQA. Experimental results that MemQ achieves state-of-the-art performance on widely used benchmarks WebQSP and CWQ.</abstract>
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%0 Conference Proceedings
%T Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning
%A Xu, Mufan
%A Liang, Gewen
%A Chen, Kehai
%A Wang, Wei
%A Zhou, Xun
%A Yang, Muyun
%A Zhao, Tiejun
%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 xu-etal-2025-memory
%X Large language models (LLMs) have achieved remarkable performance on knowledge graph question answering (KGQA) tasks by planning and interacting with knowledge graphs. However, existing methods often confuse tool utilization with knowledge reasoning, harming readability of model outputs and giving rise to hallucinatory tool invocations, which hinder the advancement of KGQA. To address this issue, we propose Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning (MemQ) to decouple LLM from tool invocation tasks using LLM-built query memory. By establishing a memory module with explicit descriptions of query statements, the proposed MemQ facilitates the KGQA process with natural language reasoning and memory-augmented query reconstruction. Meanwhile, we design an effective and readable reasoning to enhance the LLM’s reasoning capability in KGQA. Experimental results that MemQ achieves state-of-the-art performance on widely used benchmarks WebQSP and CWQ.
%R 10.18653/v1/2025.findings-acl.1234
%U https://aclanthology.org/2025.findings-acl.1234/
%U https://doi.org/10.18653/v1/2025.findings-acl.1234
%P 24068-24084
Markdown (Informal)
[Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning](https://aclanthology.org/2025.findings-acl.1234/) (Xu et al., Findings 2025)
ACL
- Mufan Xu, Gewen Liang, Kehai Chen, Wei Wang, Xun Zhou, Muyun Yang, Tiejun Zhao, and Min Zhang. 2025. Memory-augmented Query Reconstruction for LLM-based Knowledge Graph Reasoning. In Findings of the Association for Computational Linguistics: ACL 2025, pages 24068–24084, Vienna, Austria. Association for Computational Linguistics.