AI RESEARCH
Hospitality-VQA: Decision-Oriented Informativeness Evaluation for Vision-Language Models
arXiv CS.LG
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ArXi:2603.07868v1 Announce Type: cross Recent advances in Vision-Language Models (VLMs) have nstrated impressive multimodal understanding in general domains. However, their applicability to decision-oriented domains such as hospitality remains largely unexplored. In this work, we investigate how well VLMs can perform visual question answering (VQA) about hotel and facility images that are central to consumer decision-making. While many existing VQA benchmarks focus on factual correctness, they rarely capture what information users actually find useful. To address this, we first