@inproceedings{zou-etal-2025-llms,
    title = "Do {LLM}s Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews",
    author = "Zou, Chenye  and
      Wen, Xingyue  and
      Hu, Tianyi  and
      Wang, Qian Janice  and
      Hershcovich, Daniel",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.99/",
    doi = "10.18653/v1/2025.findings-emnlp.99",
    pages = "1875--1894",
    ISBN = "979-8-89176-335-7",
    abstract = "Recent advances in large language models (LLMs) have opened the door to culture-aware language tasks. We introduce the novel problem of adapting wine reviews across Chinese and English, which goes beyond literal translation by incorporating regional taste preferences and culture-specific flavor descriptors. In a case study on cross-cultural wine review adaptation, we compile the \textbf{first} parallel corpus of professional reviews, containing 8k Chinese and 16k Anglophone reviews. We benchmark both neural-machine-translation baselines and state-of-the-art LLMs with automatic metrics and human evaluation. For the latter, we propose three culture-oriented criteria{---}Cultural Proximity, Cultural Neutrality, and Cultural Genuineness{---}to assess how naturally a translated review resonates with target-culture readers. Our analysis shows that current models struggle to capture cultural nuances, especially in translating wine descriptions across different cultures. This highlights the challenges and limitations of translation models in handling cultural content."
}