Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge
- URL: http://arxiv.org/abs/2404.06833v2
- Date: Fri, 18 Oct 2024 06:19:22 GMT
- Title: Does Mapo Tofu Contain Coffee? Probing LLMs for Food-related Cultural Knowledge
- Authors: Li Zhou, Taelin Karidi, Wanlong Liu, Nicolas Garneau, Yong Cao, Wenyu Chen, Haizhou Li, Daniel Hershcovich,
- Abstract summary: 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.
- Score: 47.57055368312541
- License:
- Abstract: 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.
Related papers
- Survey of Cultural Awareness in Language Models: Text and Beyond [39.77033652289063]
Large-scale deployment of large language models (LLMs) in various applications requires LLMs to be culturally sensitive to the user to ensure inclusivity.
Culture has been widely studied in psychology and anthropology, and there has been a recent surge in research on making LLMs more culturally inclusive.
arXiv Detail & Related papers (2024-10-30T16:37:50Z) - Self-Pluralising Culture Alignment for Large Language Models [36.689491885394034]
We propose CultureSPA, a framework that allows large language models to align to pluralistic cultures.
By comparing culture-aware/unaware outputs, we are able to detect and collect culture-related instances.
Extensive experiments demonstrate that CultureSPA significantly improves the alignment of LLMs to diverse cultures without compromising general abilities.
arXiv Detail & Related papers (2024-10-16T19:06:08Z) - Translating Across Cultures: LLMs for Intralingual Cultural Adaptation [12.5954253354303]
We define the task of cultural adaptation and create an evaluation framework to evaluate the performance of modern LLMs.
We analyze possible issues with automatic adaptation.
We hope that this paper will offer more insight into the cultural understanding of LLMs and their creativity in cross-cultural scenarios.
arXiv Detail & Related papers (2024-06-20T17:06:58Z) - CulturePark: Boosting Cross-cultural Understanding in Large Language Models [63.452948673344395]
This paper introduces CulturePark, an LLM-powered multi-agent communication framework for cultural data collection.
It generates high-quality cross-cultural dialogues encapsulating human beliefs, norms, and customs.
We evaluate these models across three downstream tasks: content moderation, cultural alignment, and cultural education.
arXiv Detail & Related papers (2024-05-24T01:49:02Z) - Understanding the Capabilities and Limitations of Large Language Models for Cultural Commonsense [98.09670425244462]
Large language models (LLMs) have demonstrated substantial commonsense understanding.
This paper examines the capabilities and limitations of several state-of-the-art LLMs in the context of cultural commonsense tasks.
arXiv Detail & Related papers (2024-05-07T20:28:34Z) - CULTURE-GEN: Revealing Global Cultural Perception in Language Models through Natural Language Prompting [73.94059188347582]
We uncover culture perceptions of three SOTA models on 110 countries and regions on 8 culture-related topics through culture-conditioned generations.
We discover that culture-conditioned generation consist of linguistic "markers" that distinguish marginalized cultures apart from default cultures.
arXiv Detail & Related papers (2024-04-16T00:50:43Z) - CulturalTeaming: AI-Assisted Interactive Red-Teaming for Challenging LLMs' (Lack of) Multicultural Knowledge [69.82940934994333]
We introduce CulturalTeaming, an interactive red-teaming system that leverages human-AI collaboration to build challenging evaluation dataset.
Our study reveals that CulturalTeaming's various modes of AI assistance support annotators in creating cultural questions.
CULTURALBENCH-V0.1 is a compact yet high-quality evaluation dataset with users' red-teaming attempts.
arXiv Detail & Related papers (2024-04-10T00:25:09Z) - Massively Multi-Cultural Knowledge Acquisition & LM Benchmarking [48.21982147529661]
This paper introduces a novel approach for massively multicultural knowledge acquisition.
Our method strategically navigates from densely informative Wikipedia documents on cultural topics to an extensive network of linked pages.
Our work marks an important step towards deeper understanding and bridging the gaps of cultural disparities in AI.
arXiv Detail & Related papers (2024-02-14T18:16:54Z) - Cultural Alignment in Large Language Models: An Explanatory Analysis Based on Hofstede's Cultural Dimensions [10.415002561977655]
This research proposes a Cultural Alignment Test (Hoftede's CAT) to quantify cultural alignment using Hofstede's cultural dimension framework.
We quantitatively evaluate large language models (LLMs) against the cultural dimensions of regions like the United States, China, and Arab countries.
Our results quantify the cultural alignment of LLMs and reveal the difference between LLMs in explanatory cultural dimensions.
arXiv Detail & Related papers (2023-08-25T14:50:13Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.