Bridging Cultures in the Kitchen: A Framework and Benchmark for Cross-Cultural Recipe Retrieval (2024)

Abstract: The cross-cultural adaptation of recipes is an important application of identifying and bridging cultural differences in language. The challenge lies in retaining the essence of the original recipe while also aligning with the writing and dietary habits of the target culture. Information Retrieval (IR) offers a way to address the challenge because it retrieves results from the culinary practices of the target culture while maintaining relevance to the original recipe. We introduce a novel task about cross-cultural recipe retrieval and present a unique Chinese-English cross-cultural recipe retrieval benchmark. Our benchmark is manually annotated under limited resource, utilizing various retrieval models to generate a pool of candidate results for manual annotation. The dataset provides retrieval samples that are culturally adapted but textually diverse, presenting greater challenges. We propose CARROT, a plug-and-play cultural-aware recipe information retrieval framework that incorporates cultural-aware query rewriting and re-ranking methods and evaluate it both on our benchmark and intuitive human judgments. The results show that our framework significantly enhances the preservation of the original recipe and its cultural appropriateness for the target culture. We believe these insights will significantly contribute to future research on cultural adaptation.

## introduction cooking recipes are key tools in culinary culture @xcite , which largely varies by culture and language @xcite . for example, geographical conditions significantly affect ingredients availability while culinary history shapes people's taste preferences. food choice is a complicated behavior @xcite and it is highly associated with socio-cultural factors @xcite . the fa- english recipe (recipe retrieval) 1. 2 tablespoons olive oil 2. 1 medium onion 3. 800 grams drained cooked red beans. 4. 1 liter vegetable stock. chinese recipe ingredients: 1. 适量红豆 moderate amount of red bean 2. 适量米酒 moderate amount of rice wine 3. 适量带皮老姜 moderate amount of ginger with skin the gpt-4 adapted result @xcite , still have some evident shortcomings like using rice wine and unpeeled ginger does not align with culinary practices in english-speaking culture, while the retrieval provided suitable results, includes substitutions of ingredients that align with local culture. miliarity of food products is positively associated to sensory liking @xcite . recognizing and adapting cultural differences presents both a significant importance and a challenge @xcite . merely translating recipes can lead to both semantic and cultural mismatches @xcite . as shown in figure 1 , even gpt-4, a powerful large language models (llms) and a stateof-the-art (sota) model in cross-cultural recipe adaption @xcite , still makes obvious mistakes when adapting recipes from one culture to another, e.g., the selection of ingredients and tools are not commonly used or the flavors do not align with the preferences in the target culture. we propose to use information retrieval (ir) methods to address the issue because compared to generative models, retrieved recipes from a target culture corpus naturally align more closely with the target culture in flavor, ingredients and tools. nevertheless, cross-cultural recipe ir is a challenging task due to the existing linguistic and cultural gap between the source and target. besides the challenges posed by the intrinsic gap between different languages @xcite , an even bigger challenge is the textual discrepancies caused by cultural differences in dietary habits, naming conventions, and food-related knowledge, which complicate the task. we identify three non-trivial challenges related to cross-cultural recipe retrieval: relevance assessment for cross-cultural recipes retrieval due to cultural variations in ingredients, seasonings, and cooking methods, assessing the relevance of cross-cultural recipe pairs is complex and challenging, needing clear guidelines to standardize relevance assessments. culture-aware framework to bridge cultural gaps current ir models lack awareness of the significant cultural gaps that exist across diverse culinary traditions, retrieving recipes that are textually similar but actually quite different. foot_0 ## related work cultural and recipe adaptation cultural adaptation aims at changing the text's style by the at-tributes of culture while maintaining its original meaning, it involves common ground, values and aboutness @xcite . recipe adaptation is an important application of cultural adaptation, @xcite demonstrated that recipe adaptation is a challenging task. although language models can generate fluent recipes, they struggle to use culinary knowledge in a compositional way, such as adjusting cooking actions related to the changing ingredients. palta and rudinger (2023) and @xcite underscore the complexity of integrating cultural understanding into llms, particularly in the culinary domain. @xcite propose the cross-cultural recipes adaptation task and show that prompting llms for recipe generation is the sota method for this task. they build a recipe adaptation dataset automatically using an ir model to match recipes. however, their purpose is not to propose a novel ir model-an off-the-shelf standard ir model is used and is not evaluated with respect to the retrieval task. recipe retrieval works in recipe retrieval primarily focus on cross-modal recipe retrieval @xcite , retrieving recipes by both text and images. @xcite introduce a recipe retrieval model for japan's largest recipe sharing service. their model is trained and evaluated with online search logs. these works are not primarily aimed at crosscultural scenarios, and they use online behavior logs as datasets, whereas our work requires the use of manually annotated samples. ## carrot: a cultural-aware recipe retrieval framework we propose a framework carrot: cultural-aware recipe retrieval tool, as shown in figure 2 , a plug-and-play model combining prompt-based llms and ir methods, to address the additional challenges posed by cultural differences. 5 specifically, to address c1 in section 3, we introduced query rewriting by llms. to address c2, we introduce a plug-and-play framework (no additional fine-tuning required). to address c3, we design an additional re-ranking stage. ## experiments we describe our recipe retrieval experiments and results, using the dataset introduced in section 5 and culturalrecipes @xcite , a manually annotated cross-cultural recipe adaptation dataset, to compare the results with llms generated. ## discussion the previous sota generation method in the task of cross-cultural recipe adaptation shows better relevance. however, retrieval methods are superior in consistency and cultural appropriateness. our work is the first to highlight the potential issues in using llm-generated content for recipes, as well as the potential advantages of using ir methods for cultural adaptation. we will illustrate through specific examples how retrieval methods may have advantages over generation methods in these aspects. consistency consistency mainly reflects the quality and reliability of the recipes, which determines whether people can successfully cook according to such recipes. the recipes retrieved are based on real human culinary practices, but recipes generated by llms, despite being textually close to user created recipes, still contain hallucinations, leading to not truly instructive texts for human cooking. for example, llama3 generates the cooking steps of braised beef with potato as: 4. ... covered for 1 hour or until the beef is tender. ## conclusion in this paper, we propose a novel task of crosscultural recipe retrieval, we have manually annotated a challenging and representative benchmark. furthermore, we introduce carrot, a cultural-aware recipe retrieval framework that utilizes llms to rewrite and re-rank, thereby bridging the cultural differences in recipes between two distinct cultures. our approach has robust performance on both our proposed dataset and cultural recipe adaption dataset. we also discuss the advantages of using ir methods for cultural adaptation of recipes versus direct generation using llms. we believe our work offers a new perspective on cultural adaptation.