@inproceedings{f65e3199335340cd9e705ff92b4c6272,
title = "Towards the generation of musical explanations with GPT-3",
abstract = "Open AI{\textquoteright}s language model, GPT-3, has shown great potential for many NLP tasks, with applications in many different domains. In this work we carry out a first study on GPT-3{\textquoteright}s capability to communicate musical decisions through textual explanations when prompted with a textual representation of a piece of music. Enabling a dialogue in human-AI music partnerships is an important step towards more engaging and creative human-AI interactions. Our results show that GPT-3 lacks the necessary intelligence to really understand musical decisions. A major barrier to reach a better performance is the lack of data that includes explanations of the creative process carried out by artists for musical pieces. We believe such a resource would aid the understanding and collaboration with AI music systems.",
keywords = "Explainability, GPT3, Music",
author = "Krol, {Stephen James} and Llano, {Maria Teresa} and Jon McCormack",
note = "Funding Information: Acknowledgements. The work presented here was funded by an Early Career Researcher Seed grant awarded by the Faculty of IT at Monash University. Publisher Copyright: {\textcopyright} 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.; 11th International Conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2022, held as Part of EvoStar 2022 ; Conference date: 20-04-2022 Through 22-04-2022",
year = "2022",
doi = "10.1007/978-3-031-03789-4_9",
language = "English",
isbn = "9783031037887",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "131--147",
editor = "Tiago Martins and Nereida Rodr{\'i}guez-Fern{\'a}ndez and Rebelo, {S{\'e}rgio M.}",
booktitle = "11th International Conference, EvoMUSART 2022 Held as Part of EvoStar 2022 Madrid, Spain, April 20–22, 2022 Proceedings",
}