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The new wave of Large Language Models (LLM) has offered an efficient tool tocurate sizeable conversational datasets. So far studies have mainly focused ontask-oriented or generic open-domain dialogs, and have not fully explored theability of LLMs in following complicated prompts. In this work, we focus onpersonalization, and employ LLMs to curate a dataset which is difficult andcostly to crowd-source: PersonalityChat is a synthetic conversational datasetbased upon the popular PersonaChat dataset, but conditioned on both personasand (Big-5) personality traits. Evaluating models fine-tuned on this dataset,we show that the personality trait labels can be used for trait-basedpersonalization of generative dialogue models. We also perform a head-to-headcomparison between PersonalityChat and PersonaChat, and show that training onthe distilled dataset results in more fluent and coherent dialog agents in thesmall-model regime.

PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traits