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DC Field | Value | Language |
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dc.contributor.author | Han, Ji Eun | - |
dc.contributor.author | Koh, Jun Seok | - |
dc.contributor.author | Seo, Hyeon Tae | - |
dc.contributor.author | Chang, Du Seong | - |
dc.contributor.author | Sohn, Kyung Ah | - |
dc.date.issued | 2024-01-01 | - |
dc.identifier.uri | https://aurora.ajou.ac.kr/handle/2018.oak/37105 | - |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85195944438&origin=inward | - |
dc.description.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. | - |
dc.description.sponsorship | 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). | - |
dc.language.iso | eng | - |
dc.publisher | European Language Resources Association (ELRA) | - |
dc.subject.mesh | Data generation | - |
dc.subject.mesh | Dialogue generations | - |
dc.subject.mesh | End to end | - |
dc.subject.mesh | Human like | - |
dc.subject.mesh | Language model | - |
dc.subject.mesh | Large language model | - |
dc.subject.mesh | Personality modeling | - |
dc.subject.mesh | Personality-based dialog | - |
dc.subject.mesh | Real-world scenario | - |
dc.subject.mesh | Synthetic dialog generation | - |
dc.title | PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models | - |
dc.type | Conference | - |
dc.citation.conferenceDate | 2024.5.20. ~ 2024.5.25. | - |
dc.citation.conferenceName | Joint 30th International Conference on Computational Linguistics and 14th International Conference on Language Resources and Evaluation, LREC-COLING 2024 | - |
dc.citation.edition | 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings | - |
dc.citation.endPage | 13331 | - |
dc.citation.startPage | 13321 | - |
dc.citation.title | 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings | - |
dc.identifier.bibliographicCitation | 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation, LREC-COLING 2024 - Main Conference Proceedings, pp.13321-13331 | - |
dc.identifier.scopusid | 2-s2.0-85195944438 | - |
dc.subject.keyword | large language model | - |
dc.subject.keyword | personality-based dialogue | - |
dc.subject.keyword | synthetic dialogue generation | - |
dc.type.other | Conference Paper | - |
dc.subject.subarea | Theoretical Computer Science | - |
dc.subject.subarea | Computational Theory and Mathematics | - |
dc.subject.subarea | Computer Science Applications | - |
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