ReAct: Synergizing Reasoning and Acting in Language Models - Summary

Arxiv URL: https://arxiv.org/abs/2210.03629

Authors: Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik Narasimhan, Yuan Cao

Summary:

The paper introduces ReAct, a novel prompt-based paradigm that synergizes reasoning and acting in language models for general task solving. ReAct generates both verbal reasoning traces and actions in an interleaved manner, allowing the model to perform dynamic reasoning to create, maintain, and adjust high-level plans for acting, while also interacting with external environments to incorporate additional information into reasoning. The approach is evaluated on four diverse benchmarks and outperforms prior approaches that perform either reasoning or action generation in isolation.

Key Insights & Learnings:

• ReAct combines reasoning and acting in language models for general task solving
• ReAct generates both verbal reasoning traces and actions in an interleaved manner
• ReAct outperforms prior approaches that perform either reasoning or action generation in isolation on four diverse benchmarks
• ReAct improves model interpretability, trustworthiness, and diagnosability
• ReAct has potential for further improvement with additional training data

Terms Mentioned: large language models, reasoning, acting, chain-of-thought, task solving, prompting, interpretability, trustworthiness, diagnosability

Technologies / Libraries Mentioned: Google Research, Princeton University