Building a Personalized Dialogue System with Prompt-Tuning
Tomohito Kasahara, Daisuke Kawahara, Nguyen Tung, Shengzhe Li, Kenta, Shinzato, Toshinori Sato

TL;DR
This paper presents a method for building personalized dialogue systems using prompt-tuning on large-scale language models, achieving natural, consistent responses with lower computational costs.
Contribution
It introduces prompt-tuning for personalized dialogue systems, reducing training costs while maintaining response quality across languages.
Findings
Prompt-tuning enables personalized responses.
The system performs well in English and Japanese.
Lower computational resources are required compared to fine-tuning.
Abstract
Dialogue systems without consistent responses are not fascinating. In this study, we build a dialogue system that can respond based on a given character setting (persona) to bring consistency. Considering the trend of the rapidly increasing scale of language models, we propose an approach that uses prompt-tuning, which has low learning costs, on pre-trained large-scale language models. The results of automatic and manual evaluations in English and Japanese show that it is possible to build a dialogue system with more natural and personalized responses using less computational resources than fine-tuning.
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Taxonomy
TopicsSpeech and dialogue systems · Topic Modeling · Natural Language Processing Techniques
