VisualQuest: A Diverse Image Dataset for Evaluating Visual Recognition in LLMs

• URL: http://arxiv.org/abs/2503.19936v1
• Date: Tue, 25 Mar 2025 01:23:11 GMT
• Title: VisualQuest: A Diverse Image Dataset for Evaluating Visual Recognition in LLMs
• Authors: Kelaiti Xiao, Liang Yang, Paerhati Tulajiang, Hongfei Lin,
• Abstract summary: This paper introduces VisualQuest, a novel image dataset designed to assess the ability of large language models to interpret non-traditional, stylized imagery.<n>Unlike conventional photographic benchmarks, VisualQuest challenges models with images that incorporate abstract, symbolic, and metaphorical elements.
• Score: 12.64051404166593
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces VisualQuest, a novel image dataset designed to assess the ability of large language models (LLMs) to interpret non-traditional, stylized imagery. Unlike conventional photographic benchmarks, VisualQuest challenges models with images that incorporate abstract, symbolic, and metaphorical elements, requiring the integration of domain-specific knowledge and advanced reasoning. The dataset was meticulously curated through multiple stages of filtering, annotation, and standardization to ensure high quality and diversity. Our evaluations using several state-of-the-art multimodal LLMs reveal significant performance variations that underscore the importance of both factual background knowledge and inferential capabilities in visual recognition tasks. VisualQuest thus provides a robust and comprehensive benchmark for advancing research in multimodal reasoning and model architecture design.

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