How can I understand the reAct pattern?

Chain of Thought (CoT) is a novel prompting method designed to encourage large language models (LLMs) to explain their reasoning process by providing exemplars that demonstrate reasoning. This approach often leads to more accurate results in tasks like arithmetic, commonsense, and symbolic reasoning. However, CoT's performance gains are only observed when used with models of around 100 billion parameters, as smaller models tend to produce illogical chains of thought that worsen accuracy.

ReAct, introduced by Yao et al., is a framework that allows LLMs to generate both reasoning traces and task-specific actions in an interleaved manner. By generating reasoning traces, the model can induce, track, and update action plans, handle exceptions, and interact with external sources like knowledge bases or environments. This enables LLMs to retrieve additional information, leading to more reliable and factual responses.

ReAct has proven effective across various tasks such as question answering, fact verification, and interactive decision-making benchmarks, outperforming imitation and reinforcement learning methods while maintaining human interpretability and trustworthiness.