# Cultural Adaptation of Recipes

arXiv: https://arxiv.org/abs/2310.17353 · HTML: https://mlsanity.com/paper/2310.17353/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 71 references that are in the map.
- First posted on arXiv 26 Oct 2023.

## Builds on (most cited first, 30 of 35)

- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [BLOOM: A 176B-Parameter Open-Access Multilingual Language Model](https://mlsanity.com/paper/2211.05100/) (2022)
- [mT5: A massively multilingual pre-trained text-to-text transformer](https://mlsanity.com/paper/2010.11934/) (2020)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [Multilingual Denoising Pre-training for Neural Machine Translation](https://mlsanity.com/paper/2001.08210/) (2020)
- [GLM: General Language Model Pretraining with Autoregressive Blank Infilling](https://mlsanity.com/paper/2103.10360/) (2021)
- [MPNet: Masked and Permuted Pre-training for Language Understanding](https://mlsanity.com/paper/2004.09297/) (2020)
- [GLM-130B: An Open Bilingual Pre-trained Model](https://mlsanity.com/paper/2210.02414/) (2022)
- [Word Translation Without Parallel Data](https://mlsanity.com/paper/1710.04087/) (2017)
- [Multilingual Translation with Extensible Multilingual Pretraining and Finetuning](https://mlsanity.com/paper/2008.00401/) (2020)
- [Challenges and Strategies in Cross-Cultural NLP](https://mlsanity.com/paper/2203.10020/) (2022)
- [Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://mlsanity.com/paper/2303.17466/) (2023)
- [Exploiting Similarities among Languages for Machine Translation](https://mlsanity.com/paper/1309.4168/) (2013)
- [Deep Learning for Text Style Transfer: A Survey](https://mlsanity.com/paper/2011.00416/) (2020)
- [Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://mlsanity.com/paper/2305.14456/) (2023)
- [The BigScience ROOTS Corpus: A 1.6TB Composite Multilingual Dataset](https://mlsanity.com/paper/2303.03915/) (2023)
- [Recipe1M+: A Dataset for Learning Cross-Modal Embeddings for Cooking Recipes and Food Images](https://mlsanity.com/paper/1810.06553/) (2018)
- [On the Limitations of Unsupervised Bilingual Dictionary Induction](https://mlsanity.com/paper/1805.03620/) (2018)
- [Generating Personalized Recipes from Historical User Preferences](https://mlsanity.com/paper/1909.00105/) (2019)
- [RecipeGPT: Generative Pre-training Based Cooking Recipe Generation and Evaluation System](https://mlsanity.com/paper/2003.02498/) (2020)
- [The ROOTS Search Tool: Data Transparency for LLMs](https://mlsanity.com/paper/2302.14035/) (2023)
- [Cross-Cultural Transfer Learning for Chinese Offensive Language Detection](https://mlsanity.com/paper/2303.17927/) (2023)
- [Cultural Compass: Predicting Transfer Learning Success in Offensive Language Detection with Cultural Features](https://mlsanity.com/paper/2310.06458/) (2023)
- [DLAMA: A Framework for Curating Culturally Diverse Facts for Probing the Knowledge of Pretrained Language Models](https://mlsanity.com/paper/2306.05076/) (2023)
- [Language Models are Good Translators](https://mlsanity.com/paper/2106.13627/) (2021)
- [A Recipe for Creating Multimodal Aligned Datasets for Sequential Tasks](https://mlsanity.com/paper/2005.09606/) (2020)
- [SHARE: a System for Hierarchical Assistive Recipe Editing](https://mlsanity.com/paper/2105.08185/) (2021)

## Built on by (most cited first, 7 of 7)

- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [Multilingual Large Language Models: A Systematic Survey](https://mlsanity.com/paper/2411.11072/) (2024)
- [Machine Unlearning in Large Language Models](https://mlsanity.com/paper/2404.16841/) (2024)
- [KAHANI: Culturally-Nuanced Visual Storytelling Tool for Non-Western Cultures](https://mlsanity.com/paper/2410.19419/) (2024)
- [CRAFT: Cultural Russian-Oriented Dataset Adaptation for Focused Text-to-Image Generation](https://mlsanity.com/paper/2505.04851/) (2025)
- [Can LLMs Cook Jamaican Couscous? A Study of Cultural Novelty in Recipe Generation](https://mlsanity.com/paper/2602.10964/) (2026)
- [Probing Cultural Awareness in LLMs: A Case Study of Cross-Culture Aesthetic Stylistics](https://mlsanity.com/paper/2605.27296/) (2026)

## Abstract (arXiv)

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 comprised 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.