Large Language Model-based Human-Agent Collaboration for Complex Task Solving

Understand

In recent developments within the research community, the integration of Large Language Models (LLMs) in creating fully autonomous agents has garnered significant interest.

• Despite this, LLM-based agents frequently demonstrate notable shortcomings in adjusting to dynamic environments and fully grasping human needs.
• In this work, we introduce the problem of LLM-based human-agent collaboration for complex task-solving, exploring their synergistic potential.
• In addition, we propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC.