CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs

• URL: http://arxiv.org/abs/2605.06115v2
• Date: Fri, 08 May 2026 03:43:00 GMT
• Title: CrossCult-KIBench: A Benchmark for Cross-Cultural Knowledge Insertion in MLLMs
• Abstract summary: Cross-cultural knowledge insertion focuses on adapting models to specific cultural contexts while preserving original behavior in other cultures.<n> benchmark includes 9,800 image-grounded cases covering 49 culturally relevant visual scenarios across English, Chinese, and Arabic language-culture groups.<n>Memory-Conditioned Knowledge Insertion (MCKI) retrieves relevant cultural knowledge from an external memory using frozen MLLM representations.
• Score: 13.267566050457974
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Multimodal Large Language Models (MLLMs), trained primarily on English-centric data, frequently generate culturally inappropriate or misaligned responses in cross-cultural settings. To mitigate this, we introduce the task of cross-cultural knowledge insertion, which focuses on adapting models to specific cultural contexts while preserving their original behavior in other cultures. To facilitate research in this area, we introduce CrossCult-KIBench, a comprehensive evaluation benchmark for assessing both the effectiveness of knowledge insertion and its unintended side effects on non-target cultures. The benchmark includes 9,800 image-grounded cases covering 49 culturally relevant visual scenarios across English, Chinese, and Arabic language-culture groups. It supports evaluation in both single-insert and sequential-insert settings. We also propose Memory-Conditioned Knowledge Insertion (MCKI) as a baseline method. MCKI retrieves relevant cultural knowledge from an external memory using frozen MLLM representations, prepending matched entries as conditional prompts when applicable. Extensive experiments on CrossCult-KIBench reveal that current approaches struggle to balance effective cultural adaptation with behavioral preservation, highlighting a key challenge in developing culturally-aware MLLMs. Our work thus underscores an important research direction for developing more culturally adaptive and responsible MLLMs.

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