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ICLR 2025Blog Track PosterAccept

Can LLM Simulations Truly Reflect Humanity? A Deep Dive

Qian Wang, Zhenheng Tang, Bingsheng He

National University of Singapore · The Hong Kong University of Science and Technology

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摘要

Simulation powered by Large Language Models (LLMs) has become a promising method for exploring complex human social behaviors. However, the application of LLMs in simulations presents significant challenges, particularly regarding their capacity to accurately replicate the complexities of human behaviors and societal dynamics, as evidenced by recent studies highlighting discrepancies between simulated and real-world interactions. This blog rethinks LLM-based simulations by emphasizing both their limitations and the necessities for advancing LLM simulations. By critically examining these challenges, we aim to offer actionable insights and strategies for enhancing the applicability of LLM simulations in human society in the future.

论文信息

会议
ICLR 2025
年份
2025