Title: ReAct: Synergizing Reasoning and Acting in Language Models
Authors: Shunyu Yao, Jeffrey Zhao, Dian Yu et al.
Year: 2022
arXiv: 2210.03629 (https://arxiv.org/abs/2210.03629)

Abstract:
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: this https URL
