arXiv:2604.23443v2 Announce Type: replace Abstract: 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. In this work, we provide a theo
The proliferation of Multimodal LLMs (MLLMs) and increasing scrutiny on their performance across diverse tasks necessitates a re-evaluation of fundamental decoding strategies for specific applications like Visual Question Answering (VQA).
This research suggests that current common practices in LLM decoding might be suboptimal for certain MLLM tasks, potentially leading to more efficient and accurate model design for closed-ended, fact-based applications.
The understanding of appropriate decoding strategies for MLLMs in VQA tasks is refined, advocating for greedy decoding over stochastic sampling for improved calibration and performance in specific contexts.
- · Multimodal LLM developers
- · AI researchers in VQA
- · Applications requiring high VQA accuracy
- · One-size-fits-all MLLM decoding methodologies
- · Applications where diversity is prioritized over accuracy in closed-ended tasks
Improved accuracy and calibration in VQA systems by adopting more task-specific decoding strategies.
A broader re-evaluation of 'inherited' LLM heuristics within MLLMs for other specialized tasks, leading to optimized fine-tuning and architecture choices.
Enhanced trust and reliability in MLLM outputs for industrial applications that depend on factual accuracy, potentially accelerating adoption in specialized domains.
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Read at arXiv cs.CL