CULTURE-CMT is a Chinese‑English benchmark that evaluates contextualized translation of social‑media comments by providing the original post and conversational context. It contains 572 manually verified examples grouped into Slang, Context, and Both, allowing diagnostic analysis of translation difficulty. The benchmark also introduces a retrieval‑augmented cultural‑effectiveness Judger that combines a calibrated scoring model with dynamically retrieved cultural knowledge.
By Linjuan Wu, Dingming Li, Yiwen Qiu, Xinze Lyu, Ye Guo, Daoxin Zhang, Zhe Xu, Weiming Lu
The paper "Prompt Revision as a Source of Cultural Bias in Text-to-Image Systems" investigates how commercial text‑to‑image models silently modify user prompts before generating images, a step that is often hidden from users. Using the multilingual benchmark WORLDVIEW, the authors audit the revision layer in DALL‑E‑3, Imagen‑4, and GPT‑Image‑1.5, finding that non‑Western and non‑Anglophone contexts are disproportionately marked, reduced to narrow vocabularies, and stereotyped. The study demonstrates that the revision layer itself is a previously undocumented causal source of cultural stereotyping, underscoring the need to audit deployed systems rather than just the underlying models.
By Aleksandra Urman, Elsa Lichtenegger, Salima Jaoua, Azza Bouleimen, Robin Forsberg, Corinna Hertweck, Stefania Ionescu, Nicol\`o Pagan, Ancsa Hannak, Joachim Baumann
arXiv:2609.37287v1 Announce Type: cross
Abstract: Image translation is a fundamental capability of multimodal models for multilingual applications, requiring visual understanding and meaning preserva...
By Bo Lv, Mao Zheng, Zheng Li, Fangxu Liu, Mingrui Sun, Tao Chen
arXiv:2508. 05502v2 Announce Type: replace-cross Abstract: Multimodal Large Language Models (MLLMs) perform strongly in high-resource languages, yet often produce fluent but culturally "thin" descriptions in low-resource settings.
By Yufei Gao, Jiaying Fei, Nuo Chen, Ruirui Chen, Guohang Yan, Yunshi Lan, Botian Shi
Internet memes are a pervasive form of multimodal online communication; however, such communication often involves users from diverse linguistic and cultural backgrounds. Therefore, adapting memes acr...
TransMeme introduces a multi‑agent framework for cross‑cultural meme transcreation, addressing the unique challenges of preserving intent, adapting cultural meaning, and maintaining multimodal consistency. The system coordinates specialized agents for cultural adaptation, text rewriting, revision, and visual adjustment, and is evaluated on Chinese‑English meme pairs. Human and LLM‑based evaluations show that TransMeme outperforms baselines, achieving a 33.1% average improvement in human scores and a 60% Top‑1 ranking rate in LLM judgments.
By Jingyi Zheng, Yule Liu, Zifan Peng, Tianyi Hu, Yuemeng Zhao, Xinhu Zheng, Xinlei He