comet-b00b04e6·2 events·first seen Aliases: CoMet
A new arXiv preprint proposes a noisy-channel decomposition of Minimum Bayes Risk (MBR) decoding that breaks the process into four components: hypothesis-to-reference likelihood, reference-to-hypothesis likelihood, hypothesis prior, and reference prior. The decomposition addresses a known asymmetry problem in MBR decoding caused by directional evaluation metrics like BLEU and COMET. The framework unifies existing MBR variants under a single interpretation and suggests that channel-specific weighting could improve over standard MBR decoding.
Researchers from Princeton introduce CoMet, a post-hoc uncertainty estimation method for multimodal large language models that decomposes uncertainty into a context-specific term (prompt/task ambiguity) and a multiplicity-specific term (number of plausible answers compatible with the input). A lightweight module is trained to estimate these quantities without requiring autoregressive generation or repeated sampling, making it computationally efficient. Experiments across open-ended multimodal benchmarks, hallucination detection, and visual QA show consistent improvements over existing baselines.