PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models
<|reference_start|>PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models: 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.<|reference_end|>
@article{han2024psydial:,
    title={PSYDIAL: Personality-based Synthetic Dialogue Generation using Large
  Language Models},
    author={Ji-Eun Han, Jun-Seok Koh, Hyeon-Tae Seo, Du-Seong Chang, Kyung-Ah Sohn},
    journal={arXiv preprint arXiv:2404.00930},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.00930},
    primaryClass={cs.CL}
}