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arXiv Computation and Language By Boqi Chen, Xudong Liu, Yunke Ao, Jianing Qiu

Revisiting Greedy Decoding for Visual Question Answering: A Calibration Perspective

Read the original on arXiv Computation and Language →

The paper argues that stochastic decoding, common in large language models, is not ideal for Visual Question Answering (VQA) because VQA is a closed‑ended task with head‑heavy answer distributions and epistemic uncertainty. The authors formalize how model calibration relates to predictive accuracy and identify conditions under which greedy decoding is optimal. Experiments across multiple benchmarks show greedy decoding outperforms stochastic sampling, and a new Greedy Decoding for Reasoning Models further improves multimodal reasoning performance.

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 Computation and Language.

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