# From ChatGPT to DeepSeek: Can LLMs Simulate Humanity?

arXiv: https://arxiv.org/abs/2502.18210 · HTML: https://mlsanity.com/paper/2502.18210/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Feb 2025.

## Built on by (most cited first, 7 of 7)

- [Finetuning LLMs for Human Behavior Prediction in Social Science Experiments](https://mlsanity.com/paper/2509.05830/) (2025)
- [LLM-Based Social Simulations Require a Boundary](https://mlsanity.com/paper/2506.19806/) (2025)
- [IMPersona: Evaluating Individual Level LM Impersonation](https://mlsanity.com/paper/2504.04332/) (2025)
- [HumanLLM: Towards Personalized Understanding and Simulation of Human Nature](https://mlsanity.com/paper/2601.15793/) (2026)
- [Design and Evaluation of Generative Agent-based Platform for Human-Assistant Interaction Research: A Tale of 10 User Studies](https://mlsanity.com/paper/2505.09938/) (2025)
- [Whispers of Many Shores: Cultural Alignment through Collaborative Cultural Expertise](https://mlsanity.com/paper/2506.00242/) (2025)
- [An Agentic AI Framework for Training General Practitioner Student Skills](https://mlsanity.com/paper/2512.18440/) (2025)

## Abstract (arXiv)

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. We rethink 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.