AI RESEARCH
Revisiting Greedy Decoding for Visual Question Answering: A Calibration Perspective
arXiv CS.CL
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ArXi:2604.23443v1 Announce Type: new Stochastic sampling strategies are widely adopted in large language models (LLMs) to balance output coherence and diversity. These heuristics are often inherited in Multimodal LLMs (MLLMs) without task-specific justification. However, we contend that stochastic decoding can be suboptimal for Visual Question Answering (VQA). VQA is a closed-ended task with head-heavy answer distributions where uncertainty is usually epistemic, arising from missing or ambiguous visual evidence rather than plausible continuations.