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Towards the Generation of Musical Explanations with GPT-3

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13221)


Open AI’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’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.


  • Explainability
  • GPT3
  • Music

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The work presented here was funded by an Early Career Researcher Seed grant awarded by the Faculty of IT at Monash University.

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Correspondence to Stephen James Krol .

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Krol, S.J., Llano, M.T., McCormack, J. (2022). Towards the Generation of Musical Explanations with GPT-3. In: Martins, T., Rodríguez-Fernández, N., Rebelo, S.M. (eds) Artificial Intelligence in Music, Sound, Art and Design. EvoMUSART 2022. Lecture Notes in Computer Science, vol 13221. Springer, Cham.

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