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.

翻译：音乐与语言常存在显著相似性，这促使研究者利用预训练大语言模型（LLMs）进行符号音乐的理解与生成。尽管相关研究日益增多，但将指令调优后的LLMs应用于符号音乐的实际效果仍未得到充分评估。本研究针对基于ABC记谱法的生成与理解任务，开展了一项受控对比实验，比较了现成指令调优主干模型、领域自适应变体以及专业音乐LLM基线的性能。通过在多类符号音乐数据集和评估指标上的系统测试，我们为符号音乐应用的适应策略提供了若干见解。研究特别揭示了领域适应与保留先验信息之间的权衡关系，以及用于衡量符号音乐领域适应效果的不同指标所呈现的差异化行为特征。