MMAC: A Massive Multi-dimensional Benchmark for Audio Captioning

Abstract:With the development of audio large language models (AudioLLMs), audio captioning needs to move from brief descriptions toward open-ended and fine-grained free-form descriptions. Existing evaluations often focus on generation quality or task performance, making it difficult to diagnose information coverage and description reliability. We propose MMAC, a \textbf{M}assive \textbf{M}ulti-dimensional benchmark for \textbf{A}udio \textbf{C}aptioning. MMAC contains 5,638 audio clips from more than 20 data sources, covering 6 capability categories and 15 evaluation dimensions. Given a model-generated caption, MMAC checks whether it mentions relevant information in the target dimension and whether the mentioned content is consistent with the reference label. We evaluate representative open-source and proprietary AudioLLMs. Results show clear differences across evaluation dimensions, information coverage, and description reliability. We will release the MMAC benchmark and evaluation code.

Sound (cs.SD); Artificial Intelligence (cs.AI)

arXiv:2607.27109 [cs.SD]

(or arXiv:2607.27109v1 [cs.SD] for this version)

https://doi.org/10.48550/arXiv.2607.27109 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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