# Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge

arXiv: https://arxiv.org/abs/2404.06833 · HTML: https://mlsanity.com/paper/2404.06833/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2024.

## Built on by (most cited first, 29 of 29)

- [Survey of Cultural Awareness in Language Models: Text and Beyond](https://mlsanity.com/paper/2411.00860/) (2024)
- [Culture is Not Trivia: Sociocultural Theory for Cultural NLP](https://mlsanity.com/paper/2502.12057/) (2025)
- [CaLMQA: Exploring culturally specific long-form question answering across 23 languages](https://mlsanity.com/paper/2406.17761/) (2024)
- [Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://mlsanity.com/paper/2406.03930/) (2024)
- [FoodieQA: A Multimodal Dataset for Fine-Grained Understanding of Chinese Food Culture](https://mlsanity.com/paper/2406.11030/) (2024)
- [Multilingual Large Language Models: A Systematic Survey](https://mlsanity.com/paper/2411.11072/) (2024)
- [CARE: Multilingual Human Preference Learning for Cultural Awareness](https://mlsanity.com/paper/2504.05154/) (2025)
- [MAKIEval: A Multilingual Automatic WiKidata-based Framework for Cultural Awareness Evaluation for LLMs](https://mlsanity.com/paper/2505.21693/) (2025)
- [FoodSky: A Food-oriented Large Language Model that Passes the Chef and Dietetic Examination](https://mlsanity.com/paper/2406.10261/) (2024)
- [TALES: A Taxonomy and Analysis of Cultural Representations in LLM-generated Stories](https://mlsanity.com/paper/2511.21322/) (2025)
- [Meta-Cultural Competence: Climbing the Right Hill of Cultural Awareness](https://mlsanity.com/paper/2502.09637/) (2025)
- [Hanfu-Bench: A Multimodal Benchmark on Cross-Temporal Cultural Understanding and Transcreation](https://mlsanity.com/paper/2506.01565/) (2025)
- [CUS-QA: Local-Knowledge-Oriented Open-Ended Question Answering Dataset](https://mlsanity.com/paper/2507.22752/) (2025)
- [Isolating Culture Neurons in Multilingual Large Language Models](https://mlsanity.com/paper/2508.02241/) (2025)
- [Kalahi: A handcrafted, grassroots cultural LLM evaluation suite for Filipino](https://mlsanity.com/paper/2409.15380/) (2024)
- [What are Foundation Models Cooking in the Post-Soviet World?](https://mlsanity.com/paper/2502.18583/) (2025)
- [World in a Frame: Understanding Culture Mixing as a New Challenge for Vision-Language Models](https://mlsanity.com/paper/2511.22787/) (2025)
- [From Word to World: Evaluate and Mitigate Culture Bias in LLMs via Word Association Test](https://mlsanity.com/paper/2505.18562/) (2025)
- [Camellia: Benchmarking Cultural Biases in LLMs for Asian Languages](https://mlsanity.com/paper/2510.05291/) (2025)
- [Hierarchical Text Classification with LLM-Refined Taxonomies](https://mlsanity.com/paper/2601.18375/) (2026)
- [CulturALL: Benchmarking Multilingual and Multicultural Competence of LLMs on Grounded Tasks](https://mlsanity.com/paper/2604.19262/) (2026)
- [Mitigating Cross-Lingual Cultural Inconsistencies in LLMs via Consensus-Driven Preference Optimisation](https://mlsanity.com/paper/2605.12515/) (2026)
- [MALAMUTE: A Multilingual, Highly-granular, Template-free, Education-based Probing Dataset](https://mlsanity.com/paper/2412.10105/) (2024)
- [Do We Really Need GNNs with Explicit Structural Modeling? MLPs Suffice for Language Model Representations](https://mlsanity.com/paper/2506.21682/) (2025)
- [Do LLMs Understand Wine Descriptors Across Cultures? A Benchmark for Cultural Adaptations of Wine Reviews](https://mlsanity.com/paper/2509.12961/) (2025)
- [From Polyester Girlfriends to Blind Mice: Creating the First Pragmatics Understanding Benchmarks for Slovene](https://mlsanity.com/paper/2510.21575/) (2025)
- [To Generate or Discriminate? Methodological Considerations for Measuring Cultural Alignment in LLMs](https://mlsanity.com/paper/2601.02858/) (2026)
- [Probing Cultural Awareness in LLMs: A Case Study of Cross-Culture Aesthetic Stylistics](https://mlsanity.com/paper/2605.27296/) (2026)
- [CultureMINE: Datasets and Methods for Improving the Cultural Capabilities of NLP Systems](https://mlsanity.com/paper/2609.22494/) (2026)

## Abstract (arXiv)

Recent studies have highlighted the presence of cultural biases in Large Language Models (LLMs), yet often lack a robust methodology to dissect these phenomena comprehensively. Our work aims to bridge this gap by delving into the Food domain, a universally relevant yet culturally diverse aspect of human life. We introduce FmLAMA, a multilingual dataset centered on food-related cultural facts and variations in food practices. We analyze LLMs across various architectures and configurations, evaluating their performance in both monolingual and multilingual settings. By leveraging templates in six different languages, we investigate how LLMs interact with language-specific and cultural knowledge. Our findings reveal that (1) LLMs demonstrate a pronounced bias towards food knowledge prevalent in the United States; (2) Incorporating relevant cultural context significantly improves LLMs' ability to access cultural knowledge; (3) The efficacy of LLMs in capturing cultural nuances is highly dependent on the interplay between the probing language, the specific model architecture, and the cultural context in question. This research underscores the complexity of integrating cultural understanding into LLMs and emphasizes the importance of culturally diverse datasets to mitigate biases and enhance model performance across different cultural domains.