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@ -89,7 +89,7 @@ We can also observe that ReAct outperforms CoT on Fever and lags behind CoT on H
Prompting methods that combine and support switching between ReAct and CoT+Self-Consistency generally outperform all the other prompting methods.
## Results on Knowledge-Intensive Tasks
## Results on Decision Making Tasks
The paper also reports results demonstrating ReAct's performance on decision making tasks. ReAct is evaluated on two benchmarks called [ALFWorld](https://alfworld.github.io/) (text-based game) and [WebShop](https://webshop-pnlp.github.io/) (online shopping website environment). Both involve complex environments that require reasoning to act and explore effectively.
@ -176,4 +176,4 @@ The output we get is as follows:
We adapted the example from the [LangChain documentation](https://python.langchain.com/en/latest/modules/agents/getting_started.html), so credit goes to them. We encourage the learner to explore different combination of tools and tasks.
You can find the notebook for this code here: https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/react.ipynb
You can find the notebook for this code here: https://github.com/dair-ai/Prompt-Engineering-Guide/blob/main/notebooks/react.ipynb