MemeCULT-1K: Benchmarking South Asian Cultural Context and Humor Understanding of Multimodal Models
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Computer Science > Computation and Language
arXiv:2609.01772 (cs)
[Submitted on 1 Sep 2026]
Title:MemeCULT-1K: Benchmarking South Asian Cultural Context and Humor Understanding of Multimodal Models
Authors:Tawsif Tashwar Dipto, Mehedi Ahamed, Radib Bin Kabir, Mueeze Al Mushabbir, Mohammed Saidul Islam, Mir Rayat Imtiaz Hossain, Md Tahmid Rahman Laskar, Sabbir Ahmed
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Abstract:Meme understanding goes beyond recognizing visual content or literal text; it requires implicit cultural knowledge and pragmatic inference that most vision-language models still lack. We introduce MemeCULT-1K, a multilingual benchmark of 1,000 South Asian memes in Bengali, English, and Hindi, where each meme is paired with a cultural context note and three human-written explanations, along with a supplementary set of 54 Bengali regional dialect memes. We evaluate thirteen popular Vision Language Models (VLMs) under two settings: meme-only and context-aware. Providing minimal cultural context yields consistent gains across all models and languages: mean SBERT similarity improves from 44.6 to 56.4 (+11.8), BLEURT from 37.3 to 42.3 (+5.0), and LLM-as-a-Judge scores from 2.57 to 3.43 out of 5 (+0.86). Fine-grained error analysis reveals that closed-source models fail mainly on entity and reference misidentification, while open-source models are bottlenecked by broader cultural knowledge gaps, with linguistic and phonological failures proving the most context-resistant across both. These results highlight the difficulty of culturally grounded meme understanding and motivate future work on explicit cultural knowledge integration. Our dataset and code are publicly available at TawsifDipto17/MemeCULT-1K.
| Comments: | Accepted by EMNLP 2026 (Main Conference) |
| Subjects: | Computation and Language (cs.CL); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2609.01772 [cs.CL] |
| (or arXiv:2609.01772v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2609.01772
arXiv-issued DOI via DataCite (pending registration)
|
Submission history
From: Tawsif Tashwar Dipto [view email][v1] Tue, 1 Sep 2026 18:43:03 UTC (1,121 KB)
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