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

ACL ARR 2024 April Submission620 Authors

Abstract: 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. To tackle the problem, we propose a Reinforcement Learning-based Human-Agent Collaboration method, ReHAC, which trains a policy model designed to determine the most opportune stages for human intervention within the task-solving process. We conduct experiments under real and simulated human-agent collaboration scenarios. Experimental results demonstrate that the synergistic efforts of humans and LLM-based agents significantly improve performance in complex tasks, primarily through well-planned, limited human intervention. Datasets and code are available at: https://anonymous.4open.science/r/ReHAC/.

Paper Type: Long

Research Area: Dialogue and Interactive Systems

Research Area Keywords: human-in-the-loop, applications

Contribution Types: NLP engineering experiment, Data resources

Languages Studied: English

Submission Number: 620