Hospitality-VQA: Decision-Oriented Informativeness Evaluation for Vision-Language Models
This work addresses the problem of evaluating the decision-oriented informativeness of VLMs for consumers making hospitality-related choices, highlighting a gap in current VQA benchmarks.
This paper explores the applicability of Vision-Language Models (VLMs) to decision-oriented domains like hospitality, specifically focusing on visual question answering (VQA) about hotel and facility images. They found that state-of-the-art VLMs are not inherently decision-aware and require domain-specific finetuning to achieve reliable informativeness reasoning.
Recent advances in Vision-Language Models (VLMs) have demonstrated 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 introduce Informativeness as a formal framework to quantify how much hospitality-relevant information an image-question pair provides. Guided by this framework, we construct a new hospitality-specific VQA dataset that covers various facility types, where questions are specifically designed to reflect key user information needs. Using this benchmark, we conduct experiments with several state-of-the-art VLMs, revealing that VLMs are not intrinsically decision-aware-key visual signals remain underutilized, and reliable informativeness reasoning emerges only after modest domain-specific finetuning.