StateFlow: Enhancing LLM Task-Solving through State-Driven Workflows

arXiv:2403.11322v1 Announce Type: new
Abstract: It is a notable trend to use Large Language Models (LLMs) to tackle complex tasks, e.g., tasks that require a sequence of actions and dynamic interaction with tools and environments. In this paper, we propose StateFlow, a novel LLM-based task-solving paradigm that conceptualizes complex task-solving processes backed by LLMs as state machines. With proper construction of states and definition of state transitions, StateFlow grounds the progress of task-solving, ensuring clear tracking and management of LLMs' …

abstract arxiv cs.ai cs.cl dynamic environments language language models large language large language models llm llms novel paper paradigm processes state tasks through tools trend type workflows