PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911821174145024 |
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| author | Han, Ji-Eun Koh, Jun-Seok Seo, Hyeon-Tae Chang, Du-Seong Sohn, Kyung-Ah |
| author_facet | Han, Ji-Eun Koh, Jun-Seok Seo, Hyeon-Tae Chang, Du-Seong Sohn, Kyung-Ah |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_00930 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 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 Computation and Language 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. |
| title | PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.00930 |