  While large language models (LLMs) have demonstrated impressive capabilities
across tasks in language understanding and interactive decision making, their
abilities for reasoning (e.g. chain-of-thought prompting) and acting (e.g.
action plan generation) have primarily been studied as separate topics. In this
paper, we explore the use of LLMs to generate both reasoning traces and
task-specific actions in an interleaved manner, allowing for greater synergy
between the two: reasoning traces help the model induce, track, and update
action plans as well as handle exceptions, while actions allow it to interface
with external sources, such as knowledge bases or environments, to gather
additional information. We apply our approach, named ReAct, to a diverse set of
language and decision making tasks and demonstrate its effectiveness over
state-of-the-art baselines, as well as improved human interpretability and
trustworthiness over methods without reasoning or acting components.
Concretely, on question answering (HotpotQA) and fact verification (Fever),
ReAct overcomes issues of hallucination and error propagation prevalent in
chain-of-thought reasoning by interacting with a simple Wikipedia API, and
generates human-like task-solving trajectories that are more interpretable than
baselines without reasoning traces. On two interactive decision making
benchmarks (ALFWorld and WebShop), ReAct outperforms imitation and
reinforcement learning methods by an absolute success rate of 34% and 10%
respectively, while being prompted with only one or two in-context examples.
Project site with code: https://react-lm.github.io
