@inproceedings{chen-etal-2026-looking,
    title = "Looking Beyond the One: Operationalizing and Eliciting Visual Ambiguity in {VLLM}s",
    author = "Chen, Yuchong  and
      Zou, Bowei  and
      Chen, Yuhan  and
      Fan, Yifan  and
      Li, Xinyu  and
      Cao, Shujun  and
      Hong, Yu",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1115/",
    pages = "24306--24323",
    ISBN = "979-8-89176-390-6",
    abstract = "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."
}