CMM hallucination.

CMM is a curated benchmark designed to evaluate hallucination vulnerabilities in Large Multi-Modal Models (LMMs). It is constructed to rigorously test LMMs’ capabilities across visual, audio, and language modalities, focusing on hallucinations arising from inter-modality spurious correlations and uni-modal over-reliance.

Best published scores.

No results indexed yet — be the first to submit a score.

Neighbouring benchmarks.

Other Audio-Language Models datasets on Codesota.

Have a score that beats
this table?

Submit a checkpoint and a reproduction script. We will run it, publish the score, and — if it takes the top — annotate the step on the progress chart with your name.

• 01A public checkpoint or API endpoint
• 02A reproduction script with frozen commit + seed
• 03Declared evaluation environment (Python, deps)
• 04One row per metric declared by this dataset
• 05A contact so we can follow up on discrepancies