Self-chats from Large Language Models Make Small Emotional Support Chatbot Better

Zhonghua Zheng, Lizi Liao, Yang Deng, Libo Qin, Liqiang Nie

Abstract
Large Language Models (LLMs) have shown strong generalization abilities to excel in various tasks, including emotion support conversations. However, deploying such LLMs like GPT-3 (175B parameters) is resource-intensive and challenging at scale. In this study, we utilize LLMs as “Counseling Teacher” to enhance smaller models’ emotion support response abilities, significantly reducing the necessity of scaling up model size. To this end, we first introduce an iterative expansion framework, aiming to prompt the large teacher model to curate an expansive emotion support dialogue dataset. This curated dataset, termed ExTES, encompasses a broad spectrum of scenarios and is crafted with meticulous strategies to ensure its quality and comprehensiveness. Based on this, we then devise a Diverse Response Inpainting (DRI) mechanism to harness the teacher model to produce multiple diverse responses by filling in the masked conversation context. This richness and variety serve as instructive examples, providing a robust foundation for fine-tuning smaller student models. Experiments across varied scenarios reveal that the teacher-student scheme with DRI notably improves the response abilities of smaller models, even outperforming the teacher model in some cases. The dataset and codes are available in https://github.com/pandazzh2020/ExTES.
Anthology ID:
2024.acl-long.611
Volume:
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Month:
August
Year:
2024
Address:
Bangkok, Thailand
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
11325–11345
Language:
URL:
https://aclanthology.org/2024.acl-long.611
DOI:
10.18653/v1/2024.acl-long.611
Bibkey:
Cite (ACL):
Zhonghua Zheng, Lizi Liao, Yang Deng, Libo Qin, and Liqiang Nie. 2024. Self-chats from Large Language Models Make Small Emotional Support Chatbot Better. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 11325–11345, Bangkok, Thailand. Association for Computational Linguistics.
Cite (Informal):
Self-chats from Large Language Models Make Small Emotional Support Chatbot Better (Zheng et al., ACL 2024)
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https://preview.aclanthology.org/landing_page/2024.acl-long.611.pdf
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