Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs
- URL: http://arxiv.org/abs/2502.08045v2
- Date: Sun, 16 Feb 2025 00:34:13 GMT
- Title: Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs
- Authors: Mohsinul Kabir, Ajwad Abrar, Sophia Ananiadou,
- Abstract summary: A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs)
In this work, we challenge this constrained evaluation paradigm and explore more realistic, unconstrained approaches.
- Score: 17.673012459377375
- License:
- Abstract: A large number of studies rely on closed-style multiple-choice surveys to evaluate cultural alignment in Large Language Models (LLMs). In this work, we challenge this constrained evaluation paradigm and explore more realistic, unconstrained approaches. Using the World Values Survey (WVS) and Hofstede Cultural Dimensions as case studies, we demonstrate that LLMs exhibit stronger cultural alignment in less constrained settings, where responses are not forced. Additionally, we show that even minor changes, such as reordering survey choices, lead to inconsistent outputs, exposing the limitations of closed-style evaluations. Our findings advocate for more robust and flexible evaluation frameworks that focus on specific cultural proxies, encouraging more nuanced and accurate assessments of cultural alignment in LLMs.
Related papers
- Rethinking AI Cultural Evaluation [1.8434042562191815]
Current evaluation methods predominantly rely on multiple-choice question (MCQ) datasets.
Our findings highlight significant discrepancies between MCQ-based assessments and the values conveyed in unconstrained interactions.
We recommend moving beyond MCQs to adopt more open-ended, context-specific assessments.
arXiv Detail & Related papers (2025-01-13T23:42:37Z) - Global MMLU: Understanding and Addressing Cultural and Linguistic Biases in Multilingual Evaluation [71.59208664920452]
Cultural biases in multilingual datasets pose significant challenges for their effectiveness as global benchmarks.
We show that progress on MMLU depends heavily on learning Western-centric concepts, with 28% of all questions requiring culturally sensitive knowledge.
We release Global MMLU, an improved MMLU with evaluation coverage across 42 languages.
arXiv Detail & Related papers (2024-12-04T13:27:09Z) - LLM-GLOBE: A Benchmark Evaluating the Cultural Values Embedded in LLM Output [8.435090588116973]
We propose the LLM-GLOBE benchmark for evaluating the cultural value systems of LLMs.
We then leverage the benchmark to compare the values of Chinese and US LLMs.
Our methodology includes a novel "LLMs-as-a-Jury" pipeline which automates the evaluation of open-ended content.
arXiv Detail & Related papers (2024-11-09T01:38:55Z) - Navigating the Cultural Kaleidoscope: A Hitchhiker's Guide to Sensitivity in Large Language Models [4.771099208181585]
LLMs are increasingly deployed in global applications, ensuring users from diverse backgrounds feel respected and understood.
Cultural harm can arise when these models fail to align with specific cultural norms, resulting in misrepresentations or violations of cultural values.
We present two key contributions: A cultural harm test dataset, created to assess model outputs across different cultural contexts through scenarios that expose potential cultural insensitivities, and a culturally aligned preference dataset, aimed at restoring cultural sensitivity through fine-tuning based on feedback from diverse annotators.
arXiv Detail & Related papers (2024-10-15T18:13:10Z) - DnA-Eval: Enhancing Large Language Model Evaluation through Decomposition and Aggregation [75.81096662788254]
Large Language Models (LLMs) are scalable and economical evaluators.
The question of how reliable these evaluators are has emerged as a crucial research question.
We propose Decompose and Aggregate, which breaks down the evaluation process into different stages based on pedagogical practices.
arXiv Detail & Related papers (2024-05-24T08:12:30Z) - 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) - 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) - Political Compass or Spinning Arrow? Towards More Meaningful Evaluations for Values and Opinions in Large Language Models [61.45529177682614]
We challenge the prevailing constrained evaluation paradigm for values and opinions in large language models.
We show that models give substantively different answers when not forced.
We distill these findings into recommendations and open challenges in evaluating values and opinions in LLMs.
arXiv Detail & Related papers (2024-02-26T18:00:49Z) - CDEval: A Benchmark for Measuring the Cultural Dimensions of Large Language Models [41.885600036131045]
CDEval is a benchmark aimed at evaluating the cultural dimensions of Large Language Models.
It is constructed by incorporating both GPT-4's automated generation and human verification, covering six cultural dimensions across seven domains.
arXiv Detail & Related papers (2023-11-28T02:01:25Z) - 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.