ReAct: Synergizing Reasoning and Acting in Language Models

TL;DR

ReAct introduces a framework that combines reasoning and acting in language models, enabling better decision making, interaction with external sources, and improved interpretability across various tasks.

Contribution

The paper presents ReAct, a novel approach that interleaves reasoning and acting in language models, enhancing performance and interpretability in decision-making tasks.

Findings

ReAct outperforms state-of-the-art baselines on question answering and fact verification.

ReAct effectively interacts with external knowledge bases to reduce hallucinations.

ReAct achieves higher success rates in interactive decision-making benchmarks.

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: https://react-lm.github.io