CMI-Bench: A Comprehensive Benchmark for Evaluating Music Instruction
  Following
Authors: name: Yinghao Ma, name: Siyou Li, name: Juntao Yu, name: Emmanouil Benetos, name: Akira Maezawa

Recent advances in audio-text large language models (LLMs) have opened new
possibilities for music understanding and generation. However, existing
benchmarks are limited in scope, often relying on simplified tasks or
multi-choice evaluations that fail to reflect the complexity of real-world
music analysis. We reinterpret a broad range of traditional MIR annotations as
instruction-following formats and introduce CMI-Bench, a comprehensive music
instruction following benchmark designed to evaluate audio-text LLMs on a
diverse set of music information retrieval (MIR) tasks. These include genre
classification, emotion regression, emotion tagging, instrument classification,
pitch estimation, key detection, lyrics transcription, melody extraction, vocal
technique recognition, instrument performance technique detection, music
tagging, music captioning, and (down)beat tracking: reflecting core challenges
in MIR research. Unlike previous benchmarks, CMI-Bench adopts standardized
evaluation metrics consistent with previous state-of-the-art MIR models,
ensuring direct comparability with supervised approaches. We provide an
evaluation toolkit supporting all open-source audio-textual LLMs, including
LTU, Qwen-audio, SALMONN, MusiLingo, etc. Experiment results reveal significant
performance gaps between LLMs and supervised models, along with their culture,
chronological and gender bias, highlighting the potential and limitations of
current models in addressing MIR tasks. CMI-Bench establishes a unified
foundation for evaluating music instruction following, driving progress in
music-aware LLMs.