Can LLMs understand LilyPond? A benchmark for symbolic music generation and understanding

Title:Can LLMs understand LilyPond? A benchmark for symbolic music generation and understanding

Abstract:Symbolic music evaluation for large language models remains fragmented across representations, datasets, and metrics. We introduce LilyBench, a LilyPond-based benchmark that jointly evaluates symbolic music generation and music understanding on the same family of open-weight LLMs. The benchmark includes a 200-prompt generation suite and ten understanding tasks adapted from ABC-Eval, covering syntax, metadata prediction, structural sequencing, and music recognition. Generation quality is evaluated using compile rate, MusPy descriptor distributions via Jensen-Shannon similarity, and LilyBERT-based Fréchet Music Distance (FMD). Experiments on four open-weight models show that executable LilyPond generation is achievable in zero-shot settings, while structural understanding tasks remain challenging despite strong performance on composer and genre recognition. Our experiments also reveal systematic disagreements between descriptor-based and embedding-based metrics, suggesting that symbolic music evaluation benefits from metric triangulation rather than single-score ranking. We release the benchmark, prompt bank, and evaluation code to support future research in symbolic music generation and understanding at this https URL

Sound (cs.SD); Computation and Language (cs.CL)

H.5.5; I.2.7; J.5

arXiv:2606.08722 [cs.SD]

(or arXiv:2606.08722v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2606.08722 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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