---
title: "Bridging Cultures in the Kitchen — Hu 2024"
description: "Adapting a recipe to another culture means keeping the identity of the dish while matching the ingredients, tastes and writing conventions of the new…"
authors: ["Tianyi Hu", "Maria Maistro", "Daniel Hershcovich"]
year: 2024
venue: "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing"
source: "https://aclanthology.org/2024.emnlp-main.61/"
doi: "10.18653/v1/2024.emnlp-main.61"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/hu-2024-bridging-cultures-kitchen-framework-benchmark/"
---
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# Bridging Cultures in the Kitchen: A Framework and Benchmark for Cross-Cultural Recipe Retrieval

**Authors:** Tianyi Hu, Maria Maistro, [Daniel Hershcovich](https://md.research.elamur.ai/free/authors/daniel-hershcovich/) · **Year:** 2024 · **Venue:** Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.emnlp-main.61/) · [PDF](https://aclanthology.org/2024.emnlp-main.61.pdf) · [DOI 10.18653/v1/2024.emnlp-main.61](https://doi.org/10.18653/v1/2024.emnlp-main.61) · [OpenAlex](https://openalex.org/W4404782736)

**Citations:** 6 citations (openalex-snapshot · 2026-10-14) · [10](#cited-by) in our library, of the 140673 papers whose bibliographies we read

## Summary
Adapting a recipe to another culture means keeping the identity of the dish while matching the ingredients, tastes and writing conventions of the new audience. Generative models such as GPT-4 often fall short here. For example, they may keep ingredients that are uncommon in the target cuisine. The authors instead treat adaptation as a search problem: given a Chinese recipe, find fitting recipes written within English-speaking culinary traditions. They define this new task with a three-level relevance scale and detailed judging guidelines. They also build a manually annotated Chinese-English benchmark, pooling candidates from several retrieval models under a limited annotation budget. Their framework, CARROT, requires no fine-tuning. LLMs rewrite the query, multiple retrievers produce ranked lists, and an LLM re-ranks the merged results by relevance and cultural fit. CARROT outperforms standard retrievers. Compared with LLM-generated adaptations, its retrieved recipes are more consistent and culturally appropriate. The work provides both an evaluation resource and a retrieval-based route to cultural adaptation in NLP.

## Key points
- **Task definition:** a source-culture recipe serves as the query and target-culture recipes serve as documents. Relevance is graded as 0 (Not Match), 1 (Partial Match) or 2 (Exact Match). Partial matches must belong to the same dish category.
- **Challenges addressed:**
  - Title mismatch caused by naming conventions (e.g., Kung Pao Chicken), ingredient substitution (taro → potato) and culture-specific food knowledge (地三鲜).
  - Scarcity of labeled data.
  - The need to rank by cultural appropriateness as well as relevance.
- **CARROT pipeline:** machine-translated titles are combined with two LLM rewrites. One rewrite regenerates the title from ingredients and steps, inspired by doc2query. The other culturally adapts the title. LLM re-ranking then follows. Tested LLMs include Llama3, Qwen and Baichuan, with Llama3 performing best.
- **Results:** CARROT-Llama3 scored best among retrieval methods on ROUGE-L, BertScore, P@1, Preservation and Consistency, and came close to the best on Cultural Appropriateness. The baseline was a translated title with a bi-encoder MPNet. Retrieval also yields diverse results; for red bean soup, the top 10 contained more than five distinct main-ingredient combinations.
- **Availability and limitations:** code and data are available at github.com/TenneyHu/CARROT. Limitations include a small benchmark, coverage of only Chinese-English, and no domain fine-tuning of the retrievers.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/)

## Cited by

Cited by

10 papers of our library cite it (of the 140673 whose bibliographies we read); 1 of them have a page here, 9 not yet.

- [Culture-Aware Machine Translation in Large Language Models: Benchmarking and Investigation](https://md.research.elamur.ai/free/papers/yuan-2026-culture-aware-machine-translation-large/)

## Cite

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{hu2024bridging,
  title = {{Bridging Cultures in the Kitchen: A Framework and Benchmark for Cross-Cultural Recipe Retrieval}},
  author = {Hu, Tianyi and Maistro, Maria and Hershcovich, Daniel},
  booktitle = {Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing},
  year = {2024},
  doi = {10.18653/v1/2024.emnlp-main.61},
  url = {https://doi.org/10.18653/v1/2024.emnlp-main.61}
}
```

- [← previous Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys](https://md.research.elamur.ai/free/papers/cao-2024-bridging-cultural-nuances-dialogue-agents/)
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- [↑ back to papers of 2024](https://md.research.elamur.ai/free/papers/2024/)

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