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PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models
  • Han, Ji Eun ;
  • Koh, Jun Seok ;
  • Seo, Hyeon Tae ;
  • Chang, Du Seong ;
  • Sohn, Kyung Ah
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Publication Year
2024-01-01
Journal
2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings
Publisher
European Language Resources Association (ELRA)
Citation
2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, pp.13321-13331
Keyword
large language modelpersonality-based dialoguesynthetic dialogue generation
Mesh Keyword
Data generationDialogue generationsEnd to endHuman likeLanguage modelLarge language modelPersonality modelingPersonality-based dialogReal-world scenarioSynthetic dialog generation
All Science Classification Codes (ASJC)
Theoretical Computer ScienceComputational Theory and MathematicsComputer Science Applications
Abstract
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.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/37105
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195944438&origin=inward
Type
Conference
Funding
This work was supported by the National Research Foundation of Korea(NRF) grant (No. NRF2022R1A2C1007434) and by the Institute of Information and Communications Technology Planning and Evaluation (IITP) under Grant 2021-0-02068 (Artificial Intelligence Innovation Hub) and under the Artificial Intelligence Convergence Innovation Human Resources Development (IITP-2023-RS-2023-00255968) grant, funded by the Korea government(MSIT). This work was also supported by Institute of Information & communications Technology Planning & Evaluation (IITP) grant funded by the Korea government(MSIT) (RS-2022-00143911,AI Excellence Global Innovative Leader Education Program).
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Sohn, Kyung-Ah손경아
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