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