MMAC: A Massive Multi-dimensional Benchmark for Audio Captioning
Score
75提出新的多维评估框架，填补了现有音频字幕评估中信息覆盖度和可靠性诊断的空白。
数据集规模大、来源多样，评估维度设计系统且逻辑清晰，方法论文献详实。
为音频大语言模型研发和对比提供了标准化工具，可直接指导模型改进评估。
发表于近期arXiv，紧随AudioLLMs发展热点，及时回应了评估需求。
来自预印本平台arXiv，尚未正式发表但方法完整，开源承诺增加可信度。
Base 83.5 × T2 Source weight ×0.9 = 75
Curation line 70Selected
Why selected
This work provides a large-scale, multi-dimensional evaluation framework that directly addresses limitations of existing audio captioning evaluations, offering high methodological value.
AI commentary
「What makes this paper stand out is that it pushes audio captioning evaluation from 'how good does it sound' to 'does it actually cover key information and avoid hallucination.' For teams developing or deploying audio LLMs, MMAC can serve as a health check for their models, revealing weak spots across dimensions. As an editor, I recommend everyone in multimodal evaluation keep an eye on the open-source release of this benchmark.」
Quick overview
MMAC is a massive multi-dimensional benchmark proposed in an arXiv paper for audio captioning. It contains 5,638 audio clips from over 20 data sources, covering 6 capability categories and 15 evaluation dimensions. The benchmark checks whether model-generated captions mention relevant information in the target dimension and whether the content is consistent with reference labels. Evaluation results show significant differences across models in information coverage and description reliability. The benchmark and evaluation code will be open-sourced.
- MMAC includes 5,638 audio clips from over 20 sources, covering 6 capability categories and 15 evaluation dimensions.
- The evaluation checks both information coverage and description reliability, more comprehensive than traditional single metrics.
- Open-source and proprietary audio LLMs show clear differences across multiple dimensions.
- The benchmark and code will be publicly released to standardize audio captioning evaluation.
Full text
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