PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models

• URL: http://arxiv.org/abs/2404.00930v1
• Date: Mon, 1 Apr 2024 05:19:34 GMT
• Title: PSYDIAL: Personality-based Synthetic Dialogue Generation using Large Language Models
• Authors: Ji-Eun Han, Jun-Seok Koh, Hyeon-Tae Seo, Du-Seong Chang, Kyung-Ah Sohn,
• Abstract summary: 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 introduce PSYDIAL, the first Korean dialogue dataset focused on personality-based dialogues, curated using our proposed pipeline. 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.
• Score: 4.283022729693451
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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.

Related papers

• Self-Directed Turing Test for Large Language Models [56.64615470513102]
  The Turing test examines whether AIs can exhibit human-like behaviour in natural language conversations. Traditional Turing tests adopt a rigid dialogue format where each participant sends only one message each time. This paper proposes the Self-Directed Turing Test, which extends the original test with a burst dialogue format.
  arXiv  Detail & Related papers  (2024-08-19T09:57:28Z)
• PersonalityChat: Conversation Distillation for Personalized Dialog Modeling with Facts and Traits [5.447308344436046]
  PersonalityChat is a synthetic conversational dataset based upon the popular PersonaChat dataset. We show that the personality trait labels can be used for trait-based personalization of generative dialogue models.
  arXiv  Detail & Related papers  (2024-01-14T20:35:33Z)
• Does Collaborative Human-LM Dialogue Generation Help Information Extraction from Human Dialogues? [55.28340832822234]
  Problem-solving human dialogues in real applications can be much more complex than existing Wizard-of-Oz collections. We introduce a human-in-the-loop dialogue generation framework capable of synthesizing realistic dialogues.
  arXiv  Detail & Related papers  (2023-07-13T20:02:50Z)
• Less is More: Learning to Refine Dialogue History for Personalized Dialogue Generation [57.73547958927826]
  We propose to refine the user dialogue history on a large scale, based on which we can handle more dialogue history and obtain more accurate persona information. Specifically, we design an MSP model which consists of three personal information refiners and a personalized response generator.
  arXiv  Detail & Related papers  (2022-04-18T02:02:56Z)
• DLVGen: A Dual Latent Variable Approach to Personalized Dialogue Generation [28.721411816698563]
  We propose a Dual Latent Variable Generator (DLVGen) capable of generating personalized dialogue. Unlike prior work, DLVGen models the latent distribution over potential responses as well as the latent distribution over the agent's potential persona. Empirical results show that DLVGen is capable of generating diverse responses which accurately incorporate the agent's persona.
  arXiv  Detail & Related papers  (2021-11-22T17:21:21Z)
• Pchatbot: A Large-Scale Dataset for Personalized Chatbot [49.16746174238548]
  We introduce Pchatbot, a large-scale dialogue dataset that contains two subsets collected from Weibo and Judicial forums respectively. To adapt the raw dataset to dialogue systems, we elaborately normalize the raw dataset via processes such as anonymization. The scale of Pchatbot is significantly larger than existing Chinese datasets, which might benefit the data-driven models.
  arXiv  Detail & Related papers  (2020-09-28T12:49:07Z)
• TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue [113.45485470103762]
  In this work, we unify nine human-human and multi-turn task-oriented dialogue datasets for language modeling. To better model dialogue behavior during pre-training, we incorporate user and system tokens into the masked language modeling.
  arXiv  Detail & Related papers  (2020-04-15T04:09:05Z)
• XPersona: Evaluating Multilingual Personalized Chatbot [76.00426517401894]
  We propose a multi-lingual extension of Persona-Chat, namely XPersona. Our dataset includes persona conversations in six different languages other than English for building and evaluating multilingual personalized agents.
  arXiv  Detail & Related papers  (2020-03-17T07:52:08Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.