TRIAD: Benchmarking Omni-Modal Ambiguity in Multimodal Large Language Models
TRIAD is a diagnostic benchmark for evaluating whether omni-modal models can resolve ambiguity by jointly integrating text, image, and audio.
Overview
TRIAD targets a failure mode common in multimodal evaluation: a model may appear to solve a multimodal task while actually relying on only one dominant modality. In TRIAD, each example is constructed so that the full tri-modal input is needed… See the full description on the dataset page: https://huggingface.co/datasets/triad-26/TRIAD.
