LLM能否理解跨文化的葡萄酒描述词？一个针对葡萄酒评论文化适应性的基准
Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews
AI总结：
研究提出跨文化葡萄酒评论适应性问题，构建首个8k中文与16k英文平行语料库基准，并提出三项文化评估标准，发现当前LLM和翻译模型在捕捉文化细微差别方面存在显著局限。
摘要：
大型语言模型（LLMs）的最新进展为文化感知语言任务打开了大门。我们引入了在中英文之间调整葡萄酒评论的新问题，这通过融入地域口味偏好和特定文化的风味描述词，超越了字面翻译。在跨文化葡萄酒评论适应性案例研究中，我们编制了首个专业评论平行语料库，包含8k条中文和16k条英语评论。我们使用自动指标和人工评估，对神经机器翻译基线和最先进的LLMs进行了基准测试。对于后者，我们提出了三个面向文化的标准——文化接近度、文化中立性和文化真实性——以评估翻译后的评论与目标文化读者产生共鸣的自然程度。我们的分析表明，当前模型难以捕捉文化细微差别，尤其是在翻译不同文化的葡萄酒描述时。这凸显了翻译模型在处理文化内容时的挑战与局限性。
英文摘要：
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 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
