Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues

Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, Kam-Fai Wong


Abstract
Open-domain dialogue system usually requires different sources of knowledge to generate more informative and evidential responses. However, existing knowledge-grounded dialogue systems either focus on a single knowledge source or overlook the dependency between multiple sources of knowledge, which may result in generating inconsistent or even paradoxical responses. To incorporate multiple knowledge sources and dependencies between them, we propose SAFARI, a novel framework that leverages the exceptional capabilities of large language models (LLMs) in planning, understanding, and incorporating under both supervised and unsupervised settings. Specifically, SAFARI decouples the knowledge grounding into multiple sources and response generation, which allows easy extension to various knowledge sources including the possibility of not using any sources. To study the problem, we construct a personalized knowledge-grounded dialogue dataset Knowledge Behind Persona (KBP), which is the first to consider the dependency between persona and implicit knowledge. Experimental results on the KBP dataset demonstrate that the SAFARI framework can effectively produce persona-consistent and knowledge-enhanced responses.
Anthology ID:
2023.findings-emnlp.641
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2023
Month:
December
Year:
2023
Address:
Singapore
Editors:
Houda Bouamor, Juan Pino, Kalika Bali
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
9556–9569
Language:
URL:
https://aclanthology.org/2023.findings-emnlp.641
DOI:
10.18653/v1/2023.findings-emnlp.641
Bibkey:
Cite (ACL):
Hongru Wang, Minda Hu, Yang Deng, Rui Wang, Fei Mi, Weichao Wang, Yasheng Wang, Wai-Chung Kwan, Irwin King, and Kam-Fai Wong. 2023. Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 9556–9569, Singapore. Association for Computational Linguistics.
Cite (Informal):
Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues (Wang et al., Findings 2023)
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PDF:
https://preview.aclanthology.org/ingest-acl-2023-videos/2023.findings-emnlp.641.pdf