Do LLM s Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews (2025)

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 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.

## introduction wine reviews serve as valuable guides for consumers, offering detailed insights into the characteristics of each bottle. for casual drinkers and connoisseurs alike, these reviews act as a compass, helping them navigate the vast selection of wines available. however, due to cultural influences and geographical distinctions, beverage consumption patterns and individual preferences vary significantly across regions @xcite , not to mention reviews. these variations extend beyond mere differences in taste and are deeply rooted in the cultural, social, and environmental contexts of each region. consequently, consumer preferences for certain beverages, including wine, can differ greatly, often rendering generalized reviews less relevant or applicable across diverse audiences. professional reviews play a greater role than user reviews in promoting consumer purchases @xcite . for chinese wine consumers, professional wine reviews in chinese are scarce, and most available reviews require a paid subscription. similarly, western consumers face challenges finding professional reviews of chinese wines. yet, interest in chinese wine is rapidly growing @xcite . consumer-research evidence underscores why accessible, comprehensible reviews matter @xcite . these trends underscore the increasing global relevance of chinese wine and highlight the need for better information accessibility and cross-cultural understanding. while some may regard wine tasting as a niche domain, recent works have shown that food and drink constitute an active and important area of nlp research @xcite @xcite , as they provide rich, culturally nuanced language ideal for studying subjectivity and cultural adaptation. despite this growing interest, existing studies have not addressed the specific challenges of cross-cultural adaptation in professional wine tasting notes, which are prototypical examples of culturally shaped subjective expressions. our work fills this gap by providing a specialized dataset and empirical analyses, offering insights that extend beyond wine to other food, drink, and culturally sensitive domains in nlp. our initial goal is to address the following research question rq1: to what extent do chinese and western professional wine reviews differ? to answer rq1, we propose the first crosscultural multilingual wine reviews dataset cultur-alwr, covering over 5k wines and about 25k reviews. we then perform comprehensive data analysis on it to analyze lexical and semantic differences. we demonstrate that chinese and western reviewers tend to emphasize different flavor descriptors and adopt divergent stylistic conventions in professional writing. these findings highlight the need for cultural adaptation when translating wine reviews across languages. this paper aims to bridge these gaps by providing a comprehensive, culturally inclusive dataset of bilingual wine reviews, supporting a more globally relevant perspective on wine preferences. recognizing and adapting to cultural differences in language use is both essential and challenging @xcite , especially for subjective comments. translations of reviews using current neural machine translation systems may overlook culture-specific expressions or result in mistranslations due to insufficient grounding in physical and cultural contexts. for instance, in figure 1 , 'raspberry', a common european flavor descriptor, is rare in china ('覆盆子'), making its taste unfamiliar to chinese consumers. the flavor of raspberry is puzzling to chinese consumers, and requires additional explanation or finding a fruit with a similar flavor. 'blueberry', which has a similar flavor profile @xcite , is more popular and better understood by chinese consumers. all the outputs of this example are shown in appendix k. based on the findings, we are interested in answering the research question rq2:, to what extent do cross-cultural flavor-descriptor differences hinder target-culture consumers' comprehension of wine reviews? to measure this, we introduce three culture-related evaluation metrics -cultural proximity, cultural neutrality, and cultural genuineness and conduct a human assessment to analyze different cultures' reviews from these perspectives. we found that readers most easily understand reviews rooted in their own culture. surprisingly, western reviews are harder for chinese readers, whereas chinese reviews are rated slightly easier to understand by western readers. finally, given the lack of sensory grounding in llms @xcite , we are interested in answering the research question rq3: to what extent do llm-generated culturally adapted wine reviews enhance crosscultural alignment and reader comprehension compared to human translations? to answer this question, we prompted the llm with culture-aware instructions to translate the reviews and then conducted a blind human evaluation. according to the annotators' ratings, the llm can, under appropriate prompting, sometimes outperform literal human translations-a surprising outcome. in this work, we introduce the task of adapting wine reviews across languages and cultures. beyond direct translation, this requires adaptation concerning content and style. we focus on chinese and english wine reviews, automatically pairing reviews for the same wine from the same vintage from two monolingual corpora. as there are many reviews in english for the same wine, we also explore the inner differences in reviews from people of the same culture. we evaluate our methodology with human evaluation and automatic evaluations on the dataset we construct. our contributions are as follows: cross-cultural analysis of flavors. flavor perception varies across cultures, as shown in studies analyzing beer pairing preferences in latin america @xcite and color-flavor associations in snack packaging across china, colombia, and the uk @xcite ). research on cheddar cheese flavor lexicons across different countries @xcite further highlights cultural influences on taste perception. consumer conceptualisations of "red" and "white" diverge sharply between french, portuguese and south-african drinkers @xcite , while translation studies demonstrate how western terroir terms acquire auspicious connotations in chinese branding @xcite . these findings underscore the need for culturally adaptive approaches in wine description and translation. cultural adaptation. as culture and language are intertwined and inseparable, there is a rising demand to equip machine translation systems with greater cultural awareness @xcite @xcite . however, it is costly and time-consuming to collect culturally sensitive data and perform a human-centered evaluation @xcite . cultural adaptation in nlp focuses on adjusting text style while preserv-ing its original meaning @xcite . this is particularly important in cross-cultural tasks, where differences in rhetorical structures and communicative conventions influence translation. for culturally-aware translation, eliciting explanations has been shown to significantly enhance comprehension of culturally specific entities @xcite . recent studies showed that llms are adept at adapting cooking recipes across cultures, using a comparable and a parallel corpus @xcite . our work focuses on the translation of non-parallel subjective reviews, an area that remains underexplored. ## data collection & analysis we present culturalwr, a dataset of paired professional wine reviews. here we describe how we construct our dataset. each pair consists of one chinese review by chinese wine critics and, when available, an english review by the same authors, alongside multiple english reviews authored by western wine critics for the same wine, identified by the wine name and its corresponding vintage. ## cross-cultural wine reviews adaptation task we propose cross-cultural wine review adaptation, extending machine translation by requiring both accuracy and deliberate semantic divergence to address cultural differences. evaluating cultural adaptation is challenging, as it must balance meaning preservation with genuine cross-cultural differences. in wine review adaptation, we even need to adapt the flavor descriptors. as is common in text generation tasks, we first adopt reference-based automatic evaluation metrics. moreover, considering reference-based metrics are often unreliable for subjective tasks, we also conduct human evaluations. ## cross-cultural reviews analysis here, we perform several analyses to answer rq1 and rq2, highlighting the significant differences between chinese and western wine reviews and emphasizing the need for cultural adaptation. ## experiment to comprehensively assess the efficacy of llm translations in understanding wine and flavors, we compare various prompting strategies on tuningfree llms, alongside evaluations of an opensource machine translation model. ## results and analysis our analysis includes five parts: 1) automatic evaluation between different models; 2) fine-grained human evaluation on a subset of chinese-english for chinese-to-english translation, chatglm4 achieved the highest bleu score, suggesting strong lexical matching, while chatgpt excelled in bertscore and beer, indicating superior semantic alignment with human-written translations. for english-to-chinese translation, chatgpt led in bleu and meteor, and qwen2.5 again outperformed in bertscore, reinforcing its strength in preserving meaning. notably, chatglm4 struggled with bleu and meteor in this direction but maintained competitive bertscore values, suggesting it prioritizes semantic coherence over strict lexical overlap. additionally, the nmt system nllb still remains highly competitive. overall, chatgpt and qwen2.5 consistently demonstrated high semantic quality across both directions, while other models exhibited strengths in specific metrics, reflecting differing optimization objectives. these results highlight the inherent trade-offs between fluency, lexical fidelity, and semantic preservation across models. more importantly, they underscore that reference translations are not the sole "correct" adaptations, as translation quality is inherently subjective. this reinforces the need for a nuanced evaluation framework that accounts for cultural context, linguistic variation, and domain-specific preferences to better capture real-world translation quality. for english-to-chinese translation, chatgpt leads across most metrics, even surpassing human translations in faithfulness, grammar, and overall quality. human translations score highest in cultural genuineness, reflecting their ability to preserve nuanced expressions. qwen2.5 also remains competitive, balancing fluency and cultural adaptation. for chinese-to-english translation, chatgpt again ranks highest in faithfulness and overall quality, while mistral outperforms even human translations in cultural proximity and genuineness, suggesting a stronger emphasis on natural, idiomatic english. these results highlight trade-offs between literal accuracy and cultural adaptation. while human translations excel in cultural authenticity, llms like qwen2.5 demonstrate strong faithfulness, and mistral prioritizes fluency in the target language. this underscores the importance of context-aware evaluation that considers both linguistic accuracy and cultural nuances. for the feedback from the master of wine, we still find that model chatglm has correct vocabulary but has poor grammar, also very literal. phi has common wrong translations, but has fixed wine tasting vocabulary. llama is the most metaphorical and tend to reword stuff. mistral just skips proper nouns but eloquent. chatglm, chatgpt and human translation are best. human translation and chatgpt are maybe too literal (correct but boring), chatglm are super eloquent. ## conclusion in this work, we studied cross-cultural adaptation of wine reviews, introducing culturalwr, a dataset of paired chinese and english reviews, and evaluating llm-based adaptation methods. our results show that llms can consider cultural nuances but face challenges in maintaining detail and consistency in flavor descriptions. we also assessed adapted flavor similarities to gauge llms' understanding of wine descriptors. beyond wine reviews, our findings have broader implications for crosscultural communication in the wine industry, aiding wineries and retailers in tailoring descriptions to international audiences. this could enhance global wine marketing while preserving cultural authenticity. moreover, our work highlights ai's potential in gastronomy, paving the way for research into ai-assisted flavor profiling and food pairing. future work includes refining adaptation models for coherence, integrating multimodal data, and using user feedback to enhance ai-generated adaptations, bridging cultural gaps in wine appreciation.