The Role of Large Language Models in Musicology: Are We Ready to Trust the Machines?

Pedro Ramoneda, Emila Parada-Cabaleiro, Benno Weck, Xavier Serra


Abstract
In this work, we explore the use and reliability of Large Language Models (LLMs) in musicology. From a discussion with experts and students, we assess the current acceptance and concerns regarding this, nowadays ubiquitous, technology. We aim to go one step further, proposing a semi-automatic method to create an initial benchmark using retrieval-augmented generation models and multiple-choice question generation, validated by human experts. Our evaluation on 400 human-validated questions shows that current vanilla LLMs are less reliable than retrieval augmented generation from music dictionaries. This paper suggests that the potential of LLMs in musicology requires musicology driven research that can specialized LLMs by including accurate and reliable domain knowledge.
Anthology ID:
2024.nlp4musa-1.14
Volume:
Proceedings of the 3rd Workshop on NLP for Music and Audio (NLP4MusA)
Month:
November
Year:
2024
Address:
Oakland, USA
Editors:
Anna Kruspe, Sergio Oramas, Elena V. Epure, Mohamed Sordo, Benno Weck, SeungHeon Doh, Minz Won, Ilaria Manco, Gabriel Meseguer-Brocal
Venues:
NLP4MusA | WS
SIG:
Publisher:
Association for Computational Lingustics
Note:
Pages:
81–86
Language:
URL:
https://aclanthology.org/2024.nlp4musa-1.14/
DOI:
Bibkey:
Cite (ACL):
Pedro Ramoneda, Emila Parada-Cabaleiro, Benno Weck, and Xavier Serra. 2024. The Role of Large Language Models in Musicology: Are We Ready to Trust the Machines?. In Proceedings of the 3rd Workshop on NLP for Music and Audio (NLP4MusA), pages 81–86, Oakland, USA. Association for Computational Lingustics.
Cite (Informal):
The Role of Large Language Models in Musicology: Are We Ready to Trust the Machines? (Ramoneda et al., NLP4MusA 2024)
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PDF:
https://aclanthology.org/2024.nlp4musa-1.14.pdf