@techreport{oai:ipsj.ixsq.nii.ac.jp:00231625,
 author = {杉山, 紘次郎 and 白松, 俊},
 issue = {24},
 month = {Jan},
 note = {When a team dances on the street, they often decide by trial and error how they will move and what kind of positional configurations they will make while dancing. In this study, we developed a prototype method for generating positional configurations using a large-scale language model (LLM) in order to support such a co-creative consensus building process. Specifically, a model is constructed using GPT-3.5 fine-tuning, which takes 8 bars of music information, concepts to be expressed, genres, number of people, and other assumptions as inputs, and outputs positional configurations and dance descriptions. For this purpose, we constructed training data by dividing showcases actually danced at JAPAN DANCE DILIGHT[1] into music information, positional configuration coordinates, and dance outlines. The system proposes three patterns generated every eight measures, and the user selects the recommended positional configuration while discussing it with other dancers. By repeating this process, the system aims to provide support for dance choreography. As an evaluation experiment, we will conduct a comparison of user satisfaction and usability with the case in which the system is not used., When a team dances on the street, they often decide by trial and error how they will move and what kind of positional configurations they will make while dancing. In this study, we developed a prototype method for generating positional configurations using a large-scale language model (LLM) in order to support such a co-creative consensus building process. Specifically, a model is constructed using GPT-3.5 fine-tuning, which takes 8 bars of music information, concepts to be expressed, genres, number of people, and other assumptions as inputs, and outputs positional configurations and dance descriptions. For this purpose, we constructed training data by dividing showcases actually danced at JAPAN DANCE DILIGHT[1] into music information, positional configuration coordinates, and dance outlines. The system proposes three patterns generated every eight measures, and the user selects the recommended positional configuration while discussing it with other dancers. By repeating this process, the system aims to provide support for dance choreography. As an evaluation experiment, we will conduct a comparison of user satisfaction and usability with the case in which the system is not used.},
 title = {ストリートダンスの振り付け支援のためのLLMを用いた位置構成生成手法の試作},
 year = {2024}
}