The Curious Case of Curiosity across Human Cultures and LLMs
This addresses the problem of cultural bias in LLMs for researchers and developers in NLP, though it is incremental as it builds on existing evaluation and fine-tuning methods.
The study tackled the lack of exploration of curiosity in LLMs across cultural contexts by introducing CUEST, an evaluation framework using Yahoo! Answers data, and found that LLMs flatten cross-cultural diversity, aligning more with Western expressions, but fine-tuning strategies reduced the human-model alignment gap by up to 50%.
Recent advances in Large Language Models (LLMs) have expanded their role in human interaction, yet curiosity -- a central driver of inquiry -- remains underexplored in these systems, particularly across cultural contexts. In this work, we investigate cultural variation in curiosity using Yahoo! Answers, a real-world multi-country dataset spanning diverse topics. We introduce CUEST (CUriosity Evaluation across SocieTies), an evaluation framework that measures human-model alignment in curiosity through linguistic (style), topic preference (content) analysis and grounding insights in social science constructs. Across open- and closed-source models, we find that LLMs flatten cross-cultural diversity, aligning more closely with how curiosity is expressed in Western countries. We then explore fine-tuning strategies to induce curiosity in LLMs, narrowing the human-model alignment gap by up to 50%. Finally, we demonstrate the practical value of curiosity for LLM adaptability across cultures, showing its importance for future NLP research.