How Far Can Pretrained LLMs Go in Symbolic Music? Controlled Comparisons of Supervised and Preference-based Adaptation

TL;DR

This paper investigates the effectiveness of adapting pretrained large language models for symbolic music tasks, comparing different finetuning strategies and analyzing their impact across multiple datasets and metrics.

Contribution

It provides a controlled comparison of instruction-tuned LLMs and domain-adapted variants for symbolic music understanding and generation, highlighting adaptation tradeoffs.

Findings

Domain adaptation improves music generation quality.

Preserving prior knowledge affects adaptation success.

Evaluation metrics behave differently for music domain adaptation.

Abstract

Music often shares notable parallels with language, motivating the use of pretrained large language models (LLMs) for symbolic music understanding and generation. Despite growing interest, the practical effectiveness of adapting instruction-tuned LLMs to symbolic music remains insufficiently characterized. We present a controlled comparative study of finetuning strategies for ABC-based generation and understanding, comparing an off-the-shelf instruction-tuned backbone to domain-adapted variants and a music-specialized LLM baseline. Across multiple symbolic music corpora and evaluation signals, we provide some insights into adaptation choices for symbolic music applications. We highlight the domain adaptation vs.~preserving prior information tradeoff as well as the distinct behaviour of metrics used to measure the domain adaptation for symbolic music.