Faithful Persona-based Conversational Dataset Generation with Large Language Models

Pegah Jandaghi, Xianghai Sheng, Xinyi Bai, Jay Pujara, Hakim Sidahmed


Abstract
High-quality conversational datasets are essential for developing AI models that can communicate with users.One way to foster deeper interactions between a chatbot and its user is through *personas*, aspects of the user’s character that provide insights into their personality, motivations, and behaviors.Training Natural Language Processing (NLP) models on a diverse and comprehensive persona-based dataset can lead to conversational models that create a deeper connection with the user, and maintain their engagement. In this paper, we leverage the power of Large Language Models (LLMs) to create a large, high-quality conversational dataset from a seed dataset. We propose a Generator-Critic architecture framework to expand the initial dataset, while improving the quality of its conversations.The Generator is an LLM prompted to output conversations.The Critic consists of a mixture of expert LLMs that control the quality of the generated conversations.These experts select the best generated conversations, which we then use to improve the Generator.We release Synthetic-Persona-Chat, consisting of 20k conversations seeded from Persona-Chat.We evaluate the quality of Synthetic-Persona-Chat and our generation framework on different dimensions through extensive experiments, and observe that the losing rate of Synthetic-Persona-Chat against Persona-Chat during an AI detection test decreases from 17.2% to 8.8% over three iterations.
Anthology ID:
2024.findings-acl.904
Volume:
Findings of the Association for Computational Linguistics ACL 2024
Month:
August
Year:
2024
Address:
Bangkok, Thailand and virtual meeting
Editors:
Lun-Wei Ku, Andre Martins, Vivek Srikumar
Venue:
Findings
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Publisher:
Association for Computational Linguistics
Note:
Pages:
15245–15270
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URL:
https://aclanthology.org/2024.findings-acl.904
DOI:
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Cite (ACL):
Pegah Jandaghi, Xianghai Sheng, Xinyi Bai, Jay Pujara, and Hakim Sidahmed. 2024. Faithful Persona-based Conversational Dataset Generation with Large Language Models. In Findings of the Association for Computational Linguistics ACL 2024, pages 15245–15270, Bangkok, Thailand and virtual meeting. Association for Computational Linguistics.
Cite (Informal):
Faithful Persona-based Conversational Dataset Generation with Large Language Models (Jandaghi et al., Findings 2024)
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PDF:
https://preview.aclanthology.org/nschneid-patch-4/2024.findings-acl.904.pdf