Self-chats from large language models make small emotional support chatbot better
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”...
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sg-smu-ink.sis_research-102392024-09-02T06:47:42Z Self-chats from large language models make small emotional support chatbot better ZHENG, Zhonghua LIAO, Lizi DENG, Yang QIN, Libo NIE, Liqiang 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. 2024-08-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/9239 https://ink.library.smu.edu.sg/context/sis_research/article/10239/viewcontent/2024.acl_long.611.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Databases and Information Systems Programming Languages and Compilers |
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Databases and Information Systems Programming Languages and Compilers ZHENG, Zhonghua LIAO, Lizi DENG, Yang QIN, Libo NIE, Liqiang Self-chats from large language models make small emotional support chatbot better |
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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. |
format |
text |
author |
ZHENG, Zhonghua LIAO, Lizi DENG, Yang QIN, Libo NIE, Liqiang |
author_facet |
ZHENG, Zhonghua LIAO, Lizi DENG, Yang QIN, Libo NIE, Liqiang |
author_sort |
ZHENG, Zhonghua |
title |
Self-chats from large language models make small emotional support chatbot better |
title_short |
Self-chats from large language models make small emotional support chatbot better |
title_full |
Self-chats from large language models make small emotional support chatbot better |
title_fullStr |
Self-chats from large language models make small emotional support chatbot better |
title_full_unstemmed |
Self-chats from large language models make small emotional support chatbot better |
title_sort |
self-chats from large language models make small emotional support chatbot better |
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Institutional Knowledge at Singapore Management University |
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2024 |
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https://ink.library.smu.edu.sg/sis_research/9239 https://ink.library.smu.edu.sg/context/sis_research/article/10239/viewcontent/2024.acl_long.611.pdf |
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