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        <title>Large Language Models’ Internal Perception of Symbolic Music</title>
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            <namePart type="given">Henk</namePart>
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    <abstract>Large language models (LLMs) excel at modeling relationships between strings in natural language and have shown promise in extending to other symbolic domains like coding or mathematics. However, the extent to which they implicitly model symbolic music remains underexplored. This paper investigates how LLMs represent musical concepts by generating symbolic music data from textual prompts describing combinations of genres and styles, and evaluating their utility through recognition and generation tasks. We produce a dataset of LLM-generated MIDI files without relying on explicit musical training. We then train neural networks entirely on this LLM-generated MIDI dataset and perform genre and style classification as well as melody completion, benchmarking their performance against established models. Our results demonstrate that LLMs can infer rudimentary musical structures and temporal relationships from text, highlighting both their potential to implicitly encode musical patterns and their limitations due to a lack of explicit musical context, shedding light on their generative capabilities for symbolic music.</abstract>
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%0 Conference Proceedings
%T Large Language Models’ Internal Perception of Symbolic Music
%A Shin, Andrew
%A Kaneko, Kunitake
%Y Piperidis, Stelios
%Y Bel, Núria
%Y van den Heuvel, Henk
%Y Ide, Nancy
%Y Krek, Simon
%Y Toral, Antonio
%S Proceedings of the Fifteenth Language Resources and Evaluation Conference
%D 2026
%8 May
%I ELRA Language Resource Association
%C Palma de Mallorca, Spain
%F shin-kaneko-2026-large
%X Large language models (LLMs) excel at modeling relationships between strings in natural language and have shown promise in extending to other symbolic domains like coding or mathematics. However, the extent to which they implicitly model symbolic music remains underexplored. This paper investigates how LLMs represent musical concepts by generating symbolic music data from textual prompts describing combinations of genres and styles, and evaluating their utility through recognition and generation tasks. We produce a dataset of LLM-generated MIDI files without relying on explicit musical training. We then train neural networks entirely on this LLM-generated MIDI dataset and perform genre and style classification as well as melody completion, benchmarking their performance against established models. Our results demonstrate that LLMs can infer rudimentary musical structures and temporal relationships from text, highlighting both their potential to implicitly encode musical patterns and their limitations due to a lack of explicit musical context, shedding light on their generative capabilities for symbolic music.
%R 10.63317/495qbkvs5cdk
%U https://aclanthology.org/2026.lrec-1.733/
%U https://doi.org/10.63317/495qbkvs5cdk
%P 9339-9348

Markdown (Informal)

[Large Language Models’ Internal Perception of Symbolic Music](https://aclanthology.org/2026.lrec-1.733/) (Shin & Kaneko, LREC 2026)

• Large Language Models’ Internal Perception of Symbolic Music (Shin & Kaneko, LREC 2026)

ACL

• Andrew Shin and Kunitake Kaneko. 2026. Large Language Models’ Internal Perception of Symbolic Music. In Proceedings of the Fifteenth Language Resources and Evaluation Conference, pages 9339–9348, Palma de Mallorca, Spain. ELRA Language Resource Association.