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arXiv Machine Learning By Dengjia Zhang, Alexander Martin, William Jurayj, Kenton Murray, Benjamin Van Durme, Reno Kriz

Unified Multimodal Uncertain Inference

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Unified Multimodal Uncertain Inference (UMUI) is a new task that requires models to generate calibrated probability estimates for hypotheses conditioned on premises across text, audio, and video modalities. The authors create a human‑annotated evaluation set with scalar probability judgments for audio, visual, and audiovisual settings, and benchmark their approach on existing text and audio datasets. Their CLUE framework, which blends self‑consistent teacher calibration with distribution‑based confidence probing, enables a 3B‑parameter model to match or surpass zero‑shot baselines up to 32B parameters across all modalities.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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