Towards Measuring and Modeling "Culture" in LLMs: A Survey

• URL: http://arxiv.org/abs/2403.15412v5
• Date: Wed, 4 Sep 2024 05:12:54 GMT
• Title: Towards Measuring and Modeling "Culture" in LLMs: A Survey
• Authors: Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, Siddhant Singh, Alham Fikri Aji, Jacki O'Neill, Ashutosh Modi, Monojit Choudhury,
• Abstract summary: We present a survey of more than 90 recent papers that aim to study cultural representation and inclusion in large language models (LLMs) We observe that none of the studies explicitly define "culture" We call these aspects the proxies of culture, and organize them across two dimensions of demographic and semantic proxies.
• Score: 21.94407169332458
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: We present a survey of more than 90 recent papers that aim to study cultural representation and inclusion in large language models (LLMs). We observe that none of the studies explicitly define "culture, which is a complex, multifaceted concept; instead, they probe the models on some specially designed datasets which represent certain aspects of "culture". We call these aspects the proxies of culture, and organize them across two dimensions of demographic and semantic proxies. We also categorize the probing methods employed. Our analysis indicates that only certain aspects of ``culture,'' such as values and objectives, have been studied, leaving several other interesting and important facets, especially the multitude of semantic domains (Thompson et al., 2020) and aboutness (Hershcovich et al., 2022), unexplored. Two other crucial gaps are the lack of robustness of probing techniques and situated studies on the impact of cultural mis- and under-representation in LLM-based applications.

Related papers

• CultureVLM: Characterizing and Improving Cultural Understanding of Vision-Language Models for over 100 Countries [63.00147630084146]
  Vision-language models (VLMs) have advanced human-AI interaction but struggle with cultural understanding. CultureVerse is a large-scale multimodal benchmark covering 19, 682 cultural concepts, 188 countries/regions, 15 cultural concepts, and 3 question types. We propose CultureVLM, a series of VLMs fine-tuned on our dataset to achieve significant performance improvement in cultural understanding.
  arXiv  Detail & Related papers  (2025-01-02T14:42:37Z)
• Risks of Cultural Erasure in Large Language Models [4.613949381428196]
  We argue for the need of metricizable evaluations of language technologies that interrogate and account for historical power inequities. We probe representations that a language model produces about different places around the world when asked to describe these contexts. We analyze the cultures represented in the travel recommendations produced by a set of language model applications.
  arXiv  Detail & Related papers  (2025-01-02T04:57:50Z)
• 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)
• How Well Do LLMs Identify Cultural Unity in Diversity? [12.982460687543952]
  We introduce a benchmark dataset for evaluating decoder-only large language models (LLMs) in understanding the cultural unity of concepts. CUNIT consists of 1,425 evaluation examples building upon 285 traditional cultural-specific concepts across 10 countries. We design a contrastive matching task to evaluate the LLMs' capability to identify highly associated cross-cultural concept pairs.
  arXiv  Detail & Related papers  (2024-08-09T14:45:22Z)
• CIVICS: Building a Dataset for Examining Culturally-Informed Values in Large Language Models [59.22460740026037]
  "CIVICS: Culturally-Informed & Values-Inclusive Corpus for Societal impacts" dataset is designed to evaluate the social and cultural variation of Large Language Models (LLMs) We create a hand-crafted, multilingual dataset of value-laden prompts which address specific socially sensitive topics, including LGBTQI rights, social welfare, immigration, disability rights, and surrogacy.
  arXiv  Detail & Related papers  (2024-05-22T20:19:10Z)
• 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)
• What You Use is What You Get: Unforced Errors in Studying Cultural Aspects in Agile Software Development [2.9418191027447906]
  Investigating the influence of cultural characteristics is challenging due to the multi-faceted concept of culture. Cultural and social aspects are of high importance for their successful use in practice.
  arXiv  Detail & Related papers  (2024-04-25T20:08:37Z)
• 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)
• 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)
• Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models [89.94270049334479]
  This paper identifies a cultural dominance issue within large language models (LLMs) LLMs often provide inappropriate English-culture-related answers that are not relevant to the expected culture when users ask in non-English languages.
  arXiv  Detail & Related papers  (2023-10-19T05:38:23Z)
• 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.