Rice-VL: Evaluating Vision-Language Models for Cultural Understanding Across ASEAN Countries

• URL: http://arxiv.org/abs/2512.01419v1
• Date: Mon, 01 Dec 2025 08:55:41 GMT
• Title: Rice-VL: Evaluating Vision-Language Models for Cultural Understanding Across ASEAN Countries
• Authors: Tushar Pranav, Eshan Pandey, Austria Lyka Diane Bala, Aman Chadha, Indriyati Atmosukarto, Donny Soh Cheng Lock,
• Abstract summary: Vision-Language Models (VLMs) excel in multimodal tasks but often exhibit Western-centric biases.<n>RICE-VL is a novel benchmark evaluating VLM cultural understanding across 11 ASEAN countries.<n>We propose SEA-LAVE, an extension of the LAVE metric, assessing textual accuracy, cultural alignment, and country identification.
• Score: 12.306502538150125
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Vision-Language Models (VLMs) excel in multimodal tasks but often exhibit Western-centric biases, limiting their effectiveness in culturally diverse regions like Southeast Asia (SEA). To address this, we introduce RICE-VL, a novel benchmark evaluating VLM cultural understanding across 11 ASEAN countries. RICE-VL includes over 28,000 human-curated Visual Question Answering (VQA) samples -- covering True or False, Fill-in-the-Blank, and open-ended formats -- and 1,000 image-bounding box pairs for Visual Grounding, annotated by culturally informed experts across 14 sub-ground categories. We propose SEA-LAVE, an extension of the LAVE metric, assessing textual accuracy, cultural alignment, and country identification. Evaluations of six open- and closed-source VLMs reveal significant performance gaps in low-resource countries and abstract cultural domains. The Visual Grounding task tests models' ability to localize culturally significant elements in complex scenes, probing spatial and contextual accuracy. RICE-VL exposes limitations in VLMs' cultural comprehension and highlights the need for inclusive model development to better serve diverse global populations.

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