@inproceedings{zong-etal-2024-triad,
title = "Triad: A Framework Leveraging a Multi-Role {LLM}-based Agent to Solve Knowledge Base Question Answering",
author = "Zong, Chang and
Yan, Yuchen and
Lu, Weiming and
Shao, Jian and
Huang, Yongfeng and
Chang, Heng and
Zhuang, Yueting",
editor = "Al-Onaizan, Yaser and
Bansal, Mohit and
Chen, Yun-Nung",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-main.101",
doi = "10.18653/v1/2024.emnlp-main.101",
pages = "1698--1710",
abstract = "Recent progress with LLM-based agents has shown promising results across various tasks. However, their use in answering questions from knowledge bases remains largely unexplored. Implementing a KBQA system using traditional methods is challenging due to the shortage of task-specific training data and the complexity of creating task-focused model structures. In this paper, we present Triad, a unified framework that utilizes an LLM-based agent with multiple roles for KBQA tasks. The agent is assigned three roles to tackle different KBQA subtasks: agent as a generalist for mastering various subtasks, as a decision maker for the selection of candidates, and as an advisor for answering questions with knowledge. Our KBQA framework is executed in four phases, involving the collaboration of the agent{'}s multiple roles. We evaluated the performance of our framework using three benchmark datasets, and the results show that our framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks, yielding F1 scores of 11.8{\%} and 20.7{\%}, respectively.",
}
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<abstract>Recent progress with LLM-based agents has shown promising results across various tasks. However, their use in answering questions from knowledge bases remains largely unexplored. Implementing a KBQA system using traditional methods is challenging due to the shortage of task-specific training data and the complexity of creating task-focused model structures. In this paper, we present Triad, a unified framework that utilizes an LLM-based agent with multiple roles for KBQA tasks. The agent is assigned three roles to tackle different KBQA subtasks: agent as a generalist for mastering various subtasks, as a decision maker for the selection of candidates, and as an advisor for answering questions with knowledge. Our KBQA framework is executed in four phases, involving the collaboration of the agent’s multiple roles. We evaluated the performance of our framework using three benchmark datasets, and the results show that our framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks, yielding F1 scores of 11.8% and 20.7%, respectively.</abstract>
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%0 Conference Proceedings
%T Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering
%A Zong, Chang
%A Yan, Yuchen
%A Lu, Weiming
%A Shao, Jian
%A Huang, Yongfeng
%A Chang, Heng
%A Zhuang, Yueting
%Y Al-Onaizan, Yaser
%Y Bansal, Mohit
%Y Chen, Yun-Nung
%S Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F zong-etal-2024-triad
%X Recent progress with LLM-based agents has shown promising results across various tasks. However, their use in answering questions from knowledge bases remains largely unexplored. Implementing a KBQA system using traditional methods is challenging due to the shortage of task-specific training data and the complexity of creating task-focused model structures. In this paper, we present Triad, a unified framework that utilizes an LLM-based agent with multiple roles for KBQA tasks. The agent is assigned three roles to tackle different KBQA subtasks: agent as a generalist for mastering various subtasks, as a decision maker for the selection of candidates, and as an advisor for answering questions with knowledge. Our KBQA framework is executed in four phases, involving the collaboration of the agent’s multiple roles. We evaluated the performance of our framework using three benchmark datasets, and the results show that our framework outperforms state-of-the-art systems on the LC-QuAD and YAGO-QA benchmarks, yielding F1 scores of 11.8% and 20.7%, respectively.
%R 10.18653/v1/2024.emnlp-main.101
%U https://aclanthology.org/2024.emnlp-main.101
%U https://doi.org/10.18653/v1/2024.emnlp-main.101
%P 1698-1710
Markdown (Informal)
[Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering](https://aclanthology.org/2024.emnlp-main.101) (Zong et al., EMNLP 2024)
ACL
- Chang Zong, Yuchen Yan, Weiming Lu, Jian Shao, Yongfeng Huang, Heng Chang, and Yueting Zhuang. 2024. Triad: A Framework Leveraging a Multi-Role LLM-based Agent to Solve Knowledge Base Question Answering. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 1698–1710, Miami, Florida, USA. Association for Computational Linguistics.