LLM Based Agent
LLM-based agents are software programs that leverage large language models (LLMs) to perform complex tasks autonomously, often interacting with external tools and environments. Current research emphasizes improving agent safety and reliability through techniques like memory management, error correction, and the development of unified frameworks for agent design and evaluation, including benchmarks for assessing performance across diverse tasks and environments. This field is significant because it pushes the boundaries of AI capabilities, enabling applications in diverse areas such as social simulation, software engineering, and healthcare, while also raising important questions about AI safety and security.
Papers
The Wisdom of Partisan Crowds: Comparing Collective Intelligence in Humans and LLM-based Agents
Yun-Shiuan Chuang, Siddharth Suresh, Nikunj Harlalka, Agam Goyal, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers
Simulating Opinion Dynamics with Networks of LLM-based Agents
Yun-Shiuan Chuang, Agam Goyal, Nikunj Harlalka, Siddharth Suresh, Robert Hawkins, Sijia Yang, Dhavan Shah, Junjie Hu, Timothy T. Rogers