@inproceedings{cheng-etal-2024-call,
title = "Call Me When Necessary: {LLM}s can Efficiently and Faithfully Reason over Structured Environments",
author = "Cheng, Sitao and
Zhuang, Ziyuan and
Xu, Yong and
Yang, Fangkai and
Zhang, Chaoyun and
Qin, Xiaoting and
Huang, Xiang and
Chen, Ling and
Lin, Qingwei and
Zhang, Dongmei and
Rajmohan, Saravan and
Zhang, Qi",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.254/",
doi = "10.18653/v1/2024.findings-acl.254",
pages = "4275--4295",
abstract = "Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables. Such tasks typically require multi-hop reasoning, i.e., match natural language utterance with instances in the environment. Previous works adopt LLMs to incrementally build a reasoning path, where LLMs either invoke tools or pick up items by step-by-step interacting with the environment. We propose Reasoning-Path-Editing (Readi), a novel framework where LLMs can efficiently and faithfully reason over structured environments. In Readi, LLMs initially generate a reasoning path given a query, and edit the path only when necessary. We instantiate the path on structured environments and provide feedback to edit the path if anything goes wrong. Experimental results on three KGQA and two TableQA datasets show the effectiveness of Readi, significantly surpassing previous LLM-based methods (by 9.1{\%} Hit@1 on WebQSP, 12.4{\%} on MQA-3H and 9.5{\%} on WTQ), comparable with state-of-the-art fine-tuned methods (67{\%} on CWQ and 74.7{\%} on WebQSP) and substantially boosting the vanilla LLMs (by 14.9{\%} on CWQ). Our code will be available on \url{https://aka.ms/readi}."
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<abstract>Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables. Such tasks typically require multi-hop reasoning, i.e., match natural language utterance with instances in the environment. Previous works adopt LLMs to incrementally build a reasoning path, where LLMs either invoke tools or pick up items by step-by-step interacting with the environment. We propose Reasoning-Path-Editing (Readi), a novel framework where LLMs can efficiently and faithfully reason over structured environments. In Readi, LLMs initially generate a reasoning path given a query, and edit the path only when necessary. We instantiate the path on structured environments and provide feedback to edit the path if anything goes wrong. Experimental results on three KGQA and two TableQA datasets show the effectiveness of Readi, significantly surpassing previous LLM-based methods (by 9.1% Hit@1 on WebQSP, 12.4% on MQA-3H and 9.5% on WTQ), comparable with state-of-the-art fine-tuned methods (67% on CWQ and 74.7% on WebQSP) and substantially boosting the vanilla LLMs (by 14.9% on CWQ). Our code will be available on https://aka.ms/readi.</abstract>
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%0 Conference Proceedings
%T Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments
%A Cheng, Sitao
%A Zhuang, Ziyuan
%A Xu, Yong
%A Yang, Fangkai
%A Zhang, Chaoyun
%A Qin, Xiaoting
%A Huang, Xiang
%A Chen, Ling
%A Lin, Qingwei
%A Zhang, Dongmei
%A Rajmohan, Saravan
%A Zhang, Qi
%Y Ku, Lun-Wei
%Y Martins, Andre
%Y Srikumar, Vivek
%S Findings of the Association for Computational Linguistics: ACL 2024
%D 2024
%8 August
%I Association for Computational Linguistics
%C Bangkok, Thailand
%F cheng-etal-2024-call
%X Large Language Models (LLMs) have shown potential in reasoning over structured environments, e.g., knowledge graphs and tables. Such tasks typically require multi-hop reasoning, i.e., match natural language utterance with instances in the environment. Previous works adopt LLMs to incrementally build a reasoning path, where LLMs either invoke tools or pick up items by step-by-step interacting with the environment. We propose Reasoning-Path-Editing (Readi), a novel framework where LLMs can efficiently and faithfully reason over structured environments. In Readi, LLMs initially generate a reasoning path given a query, and edit the path only when necessary. We instantiate the path on structured environments and provide feedback to edit the path if anything goes wrong. Experimental results on three KGQA and two TableQA datasets show the effectiveness of Readi, significantly surpassing previous LLM-based methods (by 9.1% Hit@1 on WebQSP, 12.4% on MQA-3H and 9.5% on WTQ), comparable with state-of-the-art fine-tuned methods (67% on CWQ and 74.7% on WebQSP) and substantially boosting the vanilla LLMs (by 14.9% on CWQ). Our code will be available on https://aka.ms/readi.
%R 10.18653/v1/2024.findings-acl.254
%U https://aclanthology.org/2024.findings-acl.254/
%U https://doi.org/10.18653/v1/2024.findings-acl.254
%P 4275-4295
Markdown (Informal)
[Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments](https://aclanthology.org/2024.findings-acl.254/) (Cheng et al., Findings 2024)
ACL
- Sitao Cheng, Ziyuan Zhuang, Yong Xu, Fangkai Yang, Chaoyun Zhang, Xiaoting Qin, Xiang Huang, Ling Chen, Qingwei Lin, Dongmei Zhang, Saravan Rajmohan, and Qi Zhang. 2024. Call Me When Necessary: LLMs can Efficiently and Faithfully Reason over Structured Environments. In Findings of the Association for Computational Linguistics: ACL 2024, pages 4275–4295, Bangkok, Thailand. Association for Computational Linguistics.