Break the Checkbox: Challenging Closed-Style Evaluations of Cultural Alignment in LLMs

2025-02-12

AI Summary

This paper identifies critical limitations of closed-ended multiple-choice assessments for evaluating LLMs’ cultural alignment: such metrics are highly sensitive to minor design choices—e.g., answer-option ordering—and fail to reflect genuine cultural adaptability. To address this, the authors propose a novel open, dynamic, and fine-grained evaluation paradigm that integrates World Values Survey (WVS) data, Hofstede’s cultural dimensions theory, and LLM-generated open-ended responses. Cultural alignment is quantified via consistency robustness analysis—measuring coherence and stability of culturally situated reasoning under unconstrained generation. Empirical results show that LLMs exhibit significantly stronger cultural alignment in open-generation settings than in closed-choice ones. The new framework substantially improves assessment stability and cultural sensitivity, systematically exposing the inherent fragility of closed-ended evaluation for cultural alignment. It further establishes an interpretable, scalable proxy metric system to support principled modeling and benchmarking of culturally aligned AI behavior.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Humans and AI: Learning Human Values and Preferences
• Natural Language Processing: Safety and Robustness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• Challenges closed multiple-choice cultural alignment evaluations
• Explores unconstrained settings for better cultural alignment assessment
• Advocates robust frameworks using specific cultural proxies

Innovation

• Unconstrained cultural alignment evaluation
• Less forced response settings
• Flexible evaluation frameworks