MMMG: a Comprehensive and Reliable Benchmark for Multitask Multimodal Generation
Quick summary
arXiv:2505.17613v2 Announce Type: replace Abstract: Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align with human evaluation, especially for complex tasks that involve multiple modalities. We present MMMG, the first benchmark to bring the verifiable-task paradigm to multimodal generation, spanning 4 modality combinations (image, audio, interleaved text and image, interleaved text and audio). As few multimodal outputs can be checked by programs alone, MMMG targets tasks that are either verifiable or near-verifiable: by p
Key takeaways
- arXiv:2505.17613v2 Announce Type: replace Abstract: Automatically evaluating multimodal generation presents a significant challenge, as automated metrics often struggle to align with human evaluation, especially for complex tasks that involve multiple modalities.
- We present MMMG, the first benchmark to bring the verifiable-task paradigm to multimodal generation, spanning 4 modality combinations (image, audio, interleaved text and image, interleaved text and audio).
- As few multimodal outputs can be checked by programs alone, MMMG targets tasks that are either verifiable or near-verifiable: by p
Why it matters
“MMMG: a Comprehensive and Reliable Benchmark for Multitask Multimodal Generation” should be evaluated beyond branding and benchmark scores. Its practical importance will emerge in task accuracy, latency, unit cost, safety and integration with real workflows.
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