CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph

Tong Zhou, Yubo Chen, Kang Liu, Jun Zhao

Abstract
Large language models have become integral to question-answering applications despite their propensity for generating hallucinations and factually inaccurate content. Querying knowledge graphs to reduce hallucinations in LLM meets the challenge of incomplete knowledge coverage in knowledge graphs. On the other hand, updating knowledge graphs by information extraction and knowledge graph completion faces the knowledge update misalignment issue. In this work, we introduce a collaborative augmentation framework, CogMG, leveraging knowledge graphs to address the limitations of LLMs in QA scenarios, explicitly targeting the problems of incomplete knowledge coverage and knowledge update misalignment. The LLMs identify and decompose required knowledge triples that are not present in the KG, enriching them and aligning updates with real-world demands. We demonstrate the efficacy of this approach through a supervised fine-tuned LLM within an agent framework, showing significant improvements in reducing hallucinations and enhancing factual accuracy in QA responses. Our code and video are publicly available.
Anthology ID:
2024.acl-demos.35
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Yixin Cao, Yang Feng, Deyi Xiong
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
365–373
Language:
URL:
https://aclanthology.org/2024.acl-demos.35
DOI:
Bibkey:
Cite (ACL):
Tong Zhou, Yubo Chen, Kang Liu, and Jun Zhao. 2024. CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations), pages 365–373, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
CogMG: Collaborative Augmentation Between Large Language Model and Knowledge Graph (Zhou et al., ACL 2024)
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https://preview.aclanthology.org/nschneid-patch-4/2024.acl-demos.35.pdf
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