ReAct Prompting
GPT-3 prompting code for ICLR 2023 paper ReAct: Synergizing Reasoning and Acting in Language Models.
To use ReAct for more tasks, consider trying LangChain's zero-shot ReAct Agent.
Setup
You need to first have an OpenAI API key and store it in the environment variable OPENAI_API_KEY (see here).
Package requirement: openai, and install alfworld following instructions here.
Experiments
Run {hotpotqa,fever,alfworld,webshop}.ipynb. As HotpotQA and FEVER have large validation sets, we only run 500 random examples (see notebooks). We find PaLM and GPT-3 are better at different tasks.
| HotpotQA (500 random dev, EM) | FEVER (500 random dev, EM) | AlfWorld (success rate) | WebShop (success rate) | |
|---|---|---|---|---|
| PaLM-540B (paper) | 29.4 | 62.2 | 70.9 | 40 |
| GPT-3 (davinci-002) | 30.4 | 54 | 78.4 | 35.8 |
Citation
@inproceedings{yao2023react,
title = {{ReAct}: Synergizing Reasoning and Acting in Language Models},
author = {Yao, Shunyu and Zhao, Jeffrey and Yu, Dian and Du, Nan and Shafran, Izhak and Narasimhan, Karthik and Cao, Yuan},
booktitle = {International Conference on Learning Representations (ICLR) },
year = {2023},
html = {https://arxiv.org/abs/2210.03629},
}