Yuchong Chen, Bowei Zou A*STAR, Yuhan Chen Soochow University, Yifan Fan, Xinyu Li, Shujun Cao, Yu Hong Suzhou University
Visual questions are often ambiguous: the same image–question pair may admit multiple valid answers depending on which region is referenced. However, current Visual Question Answering (VQA) systems typically collapse this ambiguity, committing to a single interpretation during decoding and evaluation. In this work, we study visual question ambiguity from a grounded, region-centric perspective. We operationalize ambiguity as the existence of multiple distinct answer-supporting regions in an image, each independently yielding a valid answer. This formulation makes ambiguity observable without requiring exhaustive multi-answer annotations. Based on this definition, we conduct a systematic empirical study of state-of-the-art Visual Large Language Models (VLLMs). We find that, under default decoding, VLLMs consistently under-report ambiguity—even when multiple valid visual groundings are present. Importantly, probing model hidden states reveals that ambiguity-related signals are already encoded in their internal representations, despite not being reliably expressed in outputs. Finally, we show that selectively activating multi-focus answering based on these signals can recover additional valid answers while avoiding excessive hallucination. Together, our results suggest that ambiguity in VQA is not merely an annotation artifact or capability limitation, but a property that VLLMs internally recognize yet often fail to surface under standard decoding assumptions.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表
Jianwen Luo Soochow University, Yongkang Jin, Yu Hong Suzhou University, Jianmin Yao
Event Argument Extraction (EAE) aims to identify event arguments and assign semantic roles under a predefined schema. Recent work formulates EAE with large language models as a structured conditional generation task and applies Reinforcement Learning with Verifiable Rewards (RLVR) to optimize sequence-level event structures. However, RLVR-based EAE supervision is coarse-grained, as a single reward is assigned to the whole event structure, while optimization happens at the token level. This misalignment causes the same reward to be applied to all tokens, including those not related to event roles or arguments, introducing noise into the gradient updates and weakening the signals for decisions critical to argument extraction. To mitigate this misalignment, we propose Hybrid Token Masking RLVR (HTMR), which selectively updates policy gradients on both high-entropy forking tokens and event-critical tokens that define event structure, along with multi-perspective reasoning. Experiments across multiple benchmarks and models show that HTMR consistently outperforms full-token and high-entropy only RLVR methods. Moreover, HTMR transfers effectively as a plug-and-play approach to other tasks such as named entity recognition and relation classification. The code is publicly available for reproducibility.
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cs.CL
Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2026 · 正式发表