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CulturePark: Boosting Cross-cultural Understanding in Large Language Models
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Cheng Li
Institute of Software, CAS
chenglicat@
&Damien Teney
Idiap Research Institute
contact@
&Linyi Yang
Westlake University
yanglinyi@
&Qingsong Wen
Squirrel AI
qingsongedu@
&Xing Xie
Microsoft Research
@
&Jindong Wang
William & Mary
jwang80@
Work done during Cheng’s internship at ponding author. Work done at MSRA.
Abstract
Cultural bias is pervasive in many large language models (LLMs), largely due to the deficiency of data representative of different cultures. Typically, cultural datasets and benchmarks are constructed either by extracting subsets of existing datasets or by aggregating from platforms such as Wikipedia and social media. However, these approaches are highly dependent on real-world data and human annotations, making them costly and difficult to scale. Inspired by cognitive theories on social communication, this paper introduces CulturePark, an LLM-powered multi-agent communication framework for cultural data collection. CulturePark simulates cross-cultural human communication with LLM-based agents playing roles in different cultures. It generates high-qu
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