Extrinsic Evaluation of Cultural Competence in Large Language Models
- URL: http://arxiv.org/abs/2406.11565v3
- Date: Thu, 03 Oct 2024 19:28:35 GMT
- Title: Extrinsic Evaluation of Cultural Competence in Large Language Models
- Authors: Shaily Bhatt, Fernando Diaz,
- Abstract summary: We focus on extrinsic evaluation of cultural competence in two text generation tasks.
We evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts.
We find weak correlations between text similarity of outputs for different countries and the cultural values of these countries.
- Score: 53.626808086522985
- License:
- Abstract: Productive interactions between diverse users and language technologies require outputs from the latter to be culturally relevant and sensitive. Prior works have evaluated models' knowledge of cultural norms, values, and artifacts, without considering how this knowledge manifests in downstream applications. In this work, we focus on extrinsic evaluation of cultural competence in two text generation tasks, open-ended question answering and story generation. We quantitatively and qualitatively evaluate model outputs when an explicit cue of culture, specifically nationality, is perturbed in the prompts. Although we find that model outputs do vary when varying nationalities and feature culturally relevant words, we also find weak correlations between text similarity of outputs for different countries and the cultural values of these countries. Finally, we discuss important considerations in designing comprehensive evaluation of cultural competence in user-facing tasks.
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) - KULTURE Bench: A Benchmark for Assessing Language Model in Korean Cultural Context [5.693660906643207]
We introduce KULTURE Bench, an evaluation framework specifically designed for Korean culture.
It is designed to assess language models' cultural comprehension and reasoning capabilities at the word, sentence, and paragraph levels.
The results show that there is still significant room for improvement in the models' understanding of texts related to the deeper aspects of Korean culture.
arXiv Detail & Related papers (2024-12-10T07:20:51Z) - CROPE: Evaluating In-Context Adaptation of Vision and Language Models to Culture-Specific Concepts [45.77570690529597]
We introduce CROPE, a visual question answering benchmark designed to probe the knowledge of culture-specific concepts.
Our evaluation of several state-of-the-art open Vision and Language models shows large performance disparities between culture-specific and common concepts.
Experiments with contextual knowledge indicate that models struggle to effectively utilize multimodal information and bind culture-specific concepts to their depictions.
arXiv Detail & Related papers (2024-10-20T17:31:19Z) - 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) - 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) - 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)
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.