ysymyth/ReAct
Jupyter notebooks demonstrating the ReAct prompting technique for enabling language models to interleave reasoning and acting.

Velocity · 7d
+3.0
★ / day
Trend
→steady
star history
This repository contains the experimental code for the ReAct (Reasoning + Acting) prompting paper published at ICLR 2023. The work investigates how language models like GPT-3 and PaLM can synergize internal reasoning with external actions to improve task performance. The notebooks run experiments on HotpotQA, FEVER, AlfWorld, and WebShop datasets, measuring accuracy and success rates across reasoning and decision-making tasks.