Bridging Cultures in the Kitchen: A Framework and Benchmark for Cross-Cultural Recipe Retrieval
Tianyi Hu, Maria Maistro, Daniel Hershcovich
摘要
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 culturalaware recipe information retrieval framework that incorporates cultural-aware query rewriting and reranking 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. 红豆汤 Red Bean Soup English Recipe (GPT4 Generate) Ingredients: 1. 1 cup red beans 2. 4 cups water 3. 1/4 cup rice wine 4. 1 piece fresh ginger (about 2 inches), unpeeled and sliced 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
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper3
- RAVENEA: A Benchmark for Multimodal Retrieval-Augmented Visual Culture UnderstandingJiaang Li, Yifei Yuan, Wenyan Li, Mohammad Aliannejadi 等ICLR 2026 · 被引用 9 次
- Culinary Crossroads: A RAG Framework for Enhancing Diversity in Cross-Cultural Recipe AdaptationTianyi Hu, Andrea Morales-Garzón, Jingyi Zheng, Maria Maistro 等ACL 2026 · 被引用 2 次
- M4-RAG: A Massive-Scale Multilingual Multi-Cultural Multimodal RAGDavid Anugraha, Patrick Amadeus Irawan, Anshul Singh, En-Shiun Annie Lee 等CVPR 2026 · 被引用 2 次
它引用的顶会 Paper9
- BERTScore: Evaluating Text Generation with BERTTianyi Zhang, Varsha Kishore, Felix Wu, Kilian Q. Weinberger 等ICLR 2020 · 被引用 8,443 次
- MPNet: Masked and Permuted Pre-training for Language UnderstandingKaitao Song, Xu Tan, Tao Qin, Jianfeng Lu 等NeurIPS 2020 · 被引用 1,957 次
- ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTOmar Khattab, Matei ZahariaSIGIR 2020 · 被引用 1,246 次
- Optimizing Dense Retrieval Model Training with Hard NegativesJingtao Zhan, Jiaxin Mao, Yiqun Liu, Jiafeng Guo 等SIGIR 2021 · 被引用 242 次
- Making Monolingual Sentence Embeddings Multilingual using Knowledge DistillationNils Reimers, Iryna GurevychEMNLP 2020 · 被引用 54 次
相关 Paper
- Hybrid Fusion with Intra- and Cross-Modality Attention for Image-Recipe RetrievalJiao Li, Xing Xu, Wei Yu, Fumin Shen 等SIGIR 2021 · 被引用 21 次
- Improving Cross-Modal Recipe Retrieval with Component-Aware Prompted CLIP EmbeddingXu Huang, Jin Liu, Zhizhong Zhang, Yuan XieACM MM 2023 · 被引用 11 次
- Mitigating Cross-modal Representation Bias for Multicultural Image-to-Recipe RetrievalQing Wang, Chong-Wah Ngo, Yu Cao, Ee-Peng LimACM MM 2025
- Evaluating and Improving Cultural Awareness of Reward Models for LLM AlignmentHongbin Zhang, Kehai Chen, Xuefeng Bai, Yang Xiang 等ICLR 2026 · 被引用 4 次
- CHEF: Cross-modal Hierarchical Embeddings for Food Domain RetrievalHai Xuan Pham, Ricardo Guerrero, Vladimir Pavlovic, Jiatong LiAAAI 2021 · 被引用 22 次
