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YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models

Created by
  • Haebom

Author

Lei Wang, Heyang Gao, Xiaohe Bo, Xu Chen, Ji-Rong Wen

Outline

YuLan-OneSim is a novel social simulator that simulates human social behavior using agents based on a large-scale language model (LLM). Compared to existing research, it allows for the creation and modification of scenarios through natural language interaction without code, and provides 50 basic scenarios across eight fields, including economics, sociology, and politics. The LLM can be fine-tuned through external feedback to improve simulation quality, and it boasts scalability to handle up to 100,000 agents. Furthermore, it provides an AI social researcher function that automates the process of setting up a simulation environment, analyzing results, and generating and reviewing technical reports simply by presenting a research topic.

Takeaways, Limitations

Takeaways:
Create and modify social simulation scenarios in natural language without writing code.
Improved accessibility with 50 basic scenarios covering a wide range of fields.
Improving simulation quality with LLM fine-tuning based on external feedback.
High scalability capable of handling up to 100,000 agents
Automating social science research processes with AI social researcher capabilities.
Limitations:
Further verification of the accuracy and reliability of LLM-based simulations is needed.
The need to assess the accuracy and objectivity of AI social researchers' interpretation of results and reporting.
Possibility of biased results in specific fields
The problem of computing resource consumption due to large-scale simulations
Possibility of scenario errors due to limitations of natural language processing
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