PersonalityChat: Conversation Distillation for Personalized Dialog
  Modeling with Facts and Traits
Ehsan Lotfi Maxime De Bruyn Jeska Buhmann Walter Daelemans
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.