PSYDIAL: Personality-based Synthetic Dialogue Generation using Large
  Language Models
Ji-Eun Han Jun-Seok Koh Hyeon-Tae Seo Du-Seong Chang Kyung-Ah Sohn
We present a novel end-to-end personality-based synthetic dialogue data
generation pipeline, specifically designed to elicit responses from large
language models via prompting. We design the prompts to generate more
human-like dialogues considering real-world scenarios when users engage with
chatbots. We introduce PSYDIAL, the first Korean dialogue dataset focused on
personality-based dialogues, curated using our proposed pipeline. Notably, we
focus on the Extraversion dimension of the Big Five personality model in our
research. Experimental results indicate that while pre-trained models and those
fine-tuned with a chit-chat dataset struggle to generate responses reflecting
personality, models trained with PSYDIAL show significant improvements. The
versatility of our pipeline extends beyond dialogue tasks, offering potential
for other non-dialogue related applications. This research opens doors for more
nuanced, personality-driven conversational AI in Korean and potentially other
languages. Our code is publicly available at
https://github.com/jiSilverH/psydial.