Cultural Adaptation of Recipes (2024)

Abstract: Building upon the considerable advances in Large Language Models (LLMs), we are now equipped to address more sophisticated tasks demanding a nuanced understanding of cross-cultural contexts. A key example is recipe adaptation, which goes beyond simple translation to include a grasp of ingredients, culinary techniques, and dietary preferences specific to a given culture. We introduce a new task involving the translation and cultural adaptation of recipes between Chinese- and English-speaking cuisines. To support this investigation, we present CulturalRecipes, a unique dataset composed of automatically paired recipes written in Mandarin Chinese and English. This dataset is further enriched with a human-written and curated test set. In this intricate task of cross-cultural recipe adaptation, we evaluate the performance of various methods, including GPT-4 and other LLMs, traditional machine translation, and information retrieval techniques. Our comprehensive analysis includes both automatic and human evaluation metrics. While GPT-4 exhibits impressive abilities in adapting Chinese recipes into English, it still lags behind human expertise when translating English recipes into Chinese. This underscores the multifaceted nature of cultural adaptations. We anticipate that these insights will significantly contribute to future research on culturally aware language models and their practical application in culturally diverse contexts.

## introduction cooking recipes are a distinct form of procedural text whose accurate interpretation depends on several factors. familiarity with ingredients and measurement units, common sense about the cooking environment, and reasoning about how tools and actions affect intermediate products in the cooking process are necessary to successfully craft a recipe. such knowledge varies by culture and language, as a result of geography, history, climate, and economy @xcite . these factors impact the frequency of ingredient usage, the available forms and cost of heat for cooking, common taste profiles, written recipe style, etc. ( §2). identifying and adapting to cultural differences in language use is important and challenging @xcite . recipe translations with current machine translation technology may gloss over culture-specific phraseology or yield mistranslations due to a lack of grounding in the physical and cultural space. literal translations are often opaque or odd: a chinese dish, (literally, 'husband and wife lung slices'), can be adapted in translation to 'sliced beef in chili sauce' for english-speaking cooks. structural patterns in recipes in different cultures (e.g., mise en place foot_0 ) additionally make straightforward recipe translation difficult: cuisines differ in dish preparation methods, and temporal dependencies between actions complicate the disentanglement of recipe actions @xcite . in this work, we introduce the task of adapting cooking recipes across languages and cultures. beyond direct translation, this requires adaptation with respect to style, ingredients, measurement units, tools, techniques, and action order preferences. focusing on recipes in chinese and english, we automatically match pairs of recipes for the same dish drawn from two monolingual corpora, and train text generation models on these pairs. we evaluate our methodology with human judgments and a suite of automatic evaluations on a gold standard test set that we construct. we provide ample evidence that recipe adaptation amounts to more than mere translation and find that models finetuned on our dataset can generate grammatical, correct, and faithful recipes, appropriately adapted across cultures. intriguingly, large language models (llms) outperform our finetuned models in both automatic and human evaluations, even without training on our paired dataset. this unexpected result opens multiple avenues for future research, including how large-scale pre-training could complement our dataset and nuanced evaluation metrics that could better capture the complexities of recipe adaptation. our contributions are as follows: our dataset, code, and trained models are available at https://github.com/coastalcph /cultural-recipes . ## cultural differences in recipes extensive cross-cultural culinary research reveals compelling differences in ingredients, measurement units, tools, and actions, each reflecting historical, geographical, and economic influences unique to each culture @xcite . for example, the historical reliance on open flame cooking in china has cultivated an array of oil-based cooking techniques exclusive to chinese cuisine. further complexities arise from culture-specific terminologies for cooking methods and dish names, which pose formidable challenges to translation and adaptation @xcite . additionally, the visual presentation of online recipes exhibits striking contrasts across different cultural contexts @xcite . delving deeper, culinary preferences also demonstrate regional patterns in flavor profiles; western cuisines tend to combine ingredients that share numerous flavor compounds, while east asian cuisines often intentionally avoid such shared compounds @xcite . these intricate cultural nuances underscore the complexity and diversity inherent in global culinary practices, thereby emphasizing the intricacy involved in adapting recipes across different cultures. examples. figure 1 presents a mandarin chinese recipe and its human-authored adaptation to american english, highlighting key differences: ## the culturalrecipes dataset our dataset, culturalrecipes, builds on two existing large-scale recipe corpora in english and chinese, respectively. we create two collections of automatically paired recipes, one for each direction of adaptation (@xmath0, which we use for training and validation in our recipe adaptation experiments ( §6). additionally, culturalrecipes incorporates a small test set of human adaptations expressly crafted for the task in each direction, serving as references in our experimental evaluation. ## corpus analysis here, we perform a data-driven analysis to investigate how the cultural differences discussed in §2 are realized in english and chinese recipe corpora through the lens of distributional semantics. ## cross-cultural recipe adaptation task we propose the task of cross-cultural recipe adaptation, which extends the task of machine translation with the requirement of divergence from the source text semantics in order to address cultural differences in the target culture. while translation studies have long considered culture @xcite , this is not yet explored in machine translation. our matched cross-lingual corpora allow us to inform recipe adaptation by both language and culture simultaneously. in §6 we adopt an end-to-end sequence-to-sequence approach to the task to establish a set of baselines since this is the dominant approach in machine translation. the evaluation of cultural adaptation should prioritize meaning preservation while allowing divergences in meaning as long as they stem from cross-cultural differences. this subjective criterion is challenging to implement, as cross-cultural differences, and by extension, the task itself, are not well-defined. as common in text generation tasks, we first adopt reference-based automatic evaluation metrics ( §5.1). furthermore, to capture structural similarity between references and predictions, we employ meaning representations for evaluation ( §5.2). crucially, since reference-based metrics are often unreliable for subjective tasks @xcite , we additionally perform human evaluation ( §5.3). ## experiments here we describe our recipe adaptation experiments and results, using the culturalrecipes dataset introduced in §3. due to their success in machine translation, we experiment with three end-to-end sequence-to-sequence classes of models to adapt recipes across cultures: (finetuned) machine translation models, finetuned multilingual encoder-decoder models, and prompt-based (zero-shot) multilingual language modeling. additionally, we evaluate the automatic matching approach used in our dataset construction. these will serve as baselines for future work on this task. ## analysis and discussion our findings reinforce previous research asserting the cultural bias of llms-specifically gpt-4-towards western, english-speaking, u.s. culture, as exemplified in the food domain @xcite @xcite . however, our results also offer a more nuanced perspective. while gpt-4 demonstrates an exceptional ability to adapt to chinese cuisine, its linguistic and semantic capabilities are outperformed by chatglm2 in @xmath1. to delve deeper into these intriguing results, this section examines the strategies these models employ in the adaptation task. ## conclusion and future work in this work, we studied the task of adapting cooking recipes across cultures. we identified dimensions relevant to this task through a data-driven analysis, including differences in ingredients, tools, methods, and measurement units. we introduced culturalrecipes, a dataset of paired chinese and english recipes, and evaluated various adaptation methods. through our experiments and analysis, we show that models can learn to consider cultural aspects, including style, when adapting recipes across cultures, with some challenges remaining in the level of detail and consistency between the different components of a recipe. we envision our dataset and baselines will be useful for both downstream applications and further studies of cultural adaptation within and beyond nlp. automatically adapting recipes from one culture to another could facilitate cross-cultural cross-pollination and broaden the horizons of potential users, serving as a bridge between people through food, and being useful to both novice and experienced cooks. furthermore, our dataset is a challenging benchmark for language models: besides the complex compositional generalization ability required for recipe adaptation @xcite , it assesses the ability of multilingual language models to adapt to target cultural characteristics, and to construct well-formed and faithful recipes. lastly, our cross-cultural comparative analysis can be extended to sociological and anthropological research. future work. as acknowledged in §2, the cultural categories we assume are highly simplistic. future work will expand our datasets to treat finer-grained differences, as well as broaden it to more languages and cultures. it will further investigate the factors that impact recipe adaptation and develop more sophisticated modeling approaches to consider them, beyond the sequence-to-sequence approaches we experimented with here. finally, our dataset can provide a starting point for related tasks, including recipe classification and retrieval. cultural categorization can be a sensitive topic so we have been careful to approach it with respect for the communities involved; we encourage future research in the area to maintain this practice. we hope that our research can contribute to a greater understanding and appreciation of diverse cultural traditions and practices related to food and cooking.