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

@inproceedings{wang-etal-2023-large,
    title = "Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues",
    author = "Wang, Hongru  and
      Hu, Minda  and
      Deng, Yang  and
      Wang, Rui  and
      Mi, Fei  and
      Wang, Weichao  and
      Wang, Yasheng  and
      Kwan, Wai-Chung  and
      King, Irwin  and
      Wong, Kam-Fai",
    editor = "Bouamor, Houda  and
      Pino, Juan  and
      Bali, Kalika",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
    month = dec,
    year = "2023",
    address = "Singapore",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.findings-emnlp.641",
    doi = "10.18653/v1/2023.findings-emnlp.641",
    pages = "9556--9569",
    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.",
}

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    <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.</abstract>
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%0 Conference Proceedings
%T Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues
%A Wang, Hongru
%A Hu, Minda
%A Deng, Yang
%A Wang, Rui
%A Mi, Fei
%A Wang, Weichao
%A Wang, Yasheng
%A Kwan, Wai-Chung
%A King, Irwin
%A Wong, Kam-Fai
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F wang-etal-2023-large
%X 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.
%R 10.18653/v1/2023.findings-emnlp.641
%U https://aclanthology.org/2023.findings-emnlp.641
%U https://doi.org/10.18653/v1/2023.findings-emnlp.641
%P 9556-9569

Markdown (Informal)

[Large Language Models as Source Planner for Personalized Knowledge-grounded Dialogues](https://aclanthology.org/2023.findings-emnlp.641) (Wang et al., Findings 2023)

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.

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