Large Language Model-based Human-Agent Collaboration for Complex Task Solving (2024)

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://github.com/XueyangFeng/ReHAC/.

## introduction in today's increasingly complex world, humans are confronted with multifaceted tasks stemming from technical, social, and economic domains. solving these complex tasks necessitates not only human interaction with the environment but also intricate decision-making processes. to alleviate human workload and enhance the automation of tasks in both professional and personal spheres, researchers have been actively developing advanced tools for human assistance @xcite . recently, the emergence of large language models (llms) @xcite al., agent human env allocator env env (a) act act (b) (c) agent human act act i need help 2023; @xcite has marked a significant milestone. llms' remarkable abilities in task understanding, planning, and reasoning @xcite have given rise to the development of llm-based autonomous agents @xcite @xcite . these agents are designed to leverage the llms' capabilities to assist humans in solving complex tasks autonomously. the llms' capabilities enable them to effectively navigate and address the complexities encountered in real-world scenarios, thereby offering substantial support in human decision-making processes of task-solving. despite the remarkable progress of llm-based agents, there remains a notable gap in their intelligence level to handle complex and dynamic realworld tasks with human-like proficiency. this limitation poses a significant challenge to their practicality in real-world applications, especially in scenarios where high accuracy is crucial, such as the legal or financial domains. addressing this challenge extends beyond just enhancing the agents' capabilities. incorporating human intuition and wisdom is equally vital for the effective management of these intricate and evolving tasks, offering a complementary approach to the limitations of current agent technologies. in this work, we introduce the problem of llmbased human-agent collaboration for complex task solving, aiming to augment the capabilities of llm-based agents by integrating human intuition and wisdom. the idea is analogous to the evolution in autonomous driving technology, which has been categorized into varying levels of autonomy, ranging from no automation, conditional automation to full automation @xcite . referring to this framework, we define different levels of human-agent collaboration for complex task solving, as illustrated in figure 1 . instead of aiming for full automation, human-agent collaboration under the paradigm of conditional automation enables humans to intervene the complex task-solving when necessary, while agents handle most of the sub-tasks. applying this conditional automation mode to llm-based agents offers a practical path for their deployment in real-world scenarios, acknowledging the current limitations in their cognitive capabilities. some researchers have made preliminary attempts, by designing heuristic rules or specialized prompts to determine the stages at which agents should seek human assistance @xcite @xcite . however, these rule-based or prompt-driven approaches are heavily reliant on specific application contexts and lack universality. they often demand a deep understanding of the domain and substantial experience from the designers, otherwise, suboptimal design choices can lead to reduced performance. apart from that, a standardized formal framework and universally accepted paradigm for leveraging large language models (llms) in human-agent collaboration is still lacking. to overcome the aforementioned challenges, we propose a reinforcement learning-based human-agent collaboration method(rehac), which is a learnable general framework aimed at dynamically identify the most advantageous stages for human intervention during the task-solving process. in the experiments, we first conduct real human-agent experiments on the hotpotqa dataset. to further assess the efficacy of our method, we conduct simulated human-agent collaboration experiments on the hotpotqa, strategyqa, and intercode datasets. in addition, we also analyze the generalization of our rehac method on different prompt frameworks and collaboration paradigms. to summarize, our experimental results indicate that with a policy model learned from limited data, rehac can effectively allocate human intervention in human-agent collaboration scenarios, thereby achieving a balance between effectiveness and efficiency (average 25.8% relative improvement over baselines on the hotpotqa dataset). ## approach in this section, we first formulate the problem of human-agent collaboration for complex task solving, and then introduce our proposed rehac method in detail. ## discussion in this section, we propose two extended research directions to enhance the effectiveness, safety, and intelligence of human-agent collaboration: development stages of llm-based agents inspired by the l1 to l5 grading model in autonomous driving, we suggest adapting this framework for llm-based human-agent collaboration. it offers a clear structure to assess the current development stage of human-agent technologies and guide future research. while llm agents have not reached high or full automation, this framework is crucial for identifying key technologies and challenges. however, our research indicates a significant gap before llm agents achieve full automation (l5). effective human-agent collaboration could be a bridge towards this goal. ## conclusion in this paper, we explore the integration of human expertise and the computational power of large language models (llms) in complex decision-making tasks. we introduce a reinforcement learning approach, rehac, for human-agent collaboration. rehac uses a learnable policy to identify key points for human intervention in task resolution. experimental results suggest that rehac outperforms traditional heuristic and prompt-based methods in human-agent tasks, providing a viable framework for applying llms in real-world scenarios.