Question What is the elevation range for the area that the eastern sector of the
Colorado orogeny extends into?
Thought 1 I need to search Colorado orogeny, find the area that the eastern sector
of the Colorado orogeny extends into, then find the elevation range of the
area.
Action 1 Search[Colorado orogeny]
Observation 1 The Colorado orogeny was an episode of mountain building (an orogeny) in
Colorado and surrounding areas.
Thought 2 It does not mention the eastern sector. So I need to look up eastern
sector.
Action 2 Lookup[eastern sector]
Observation 2 (Result 1 / 1) The eastern sector extends into the High Plains and is called
the Central Plains orogeny.
Thought 3 The eastern sector of Colorado orogeny extends into the High Plains. So I
need to search High Plains and find its elevation range.
Action 3 Search[High Plains]
Observation 3 High Plains refers to one of two distinct land regions
Thought 4 I need to instead search High Plains (United States).
Action 4 Search[High Plains (United States)]
Observation 4 The High Plains are a subregion of the Great Plains. From east to west, the
High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130
m).[3]
Thought 5 High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer
is 1,800 to 7,000 ft.
Action 5 Finish[1,800 to 7,000 ft]
...
Note that different prompts setups are used for different types of tasks. For tasks where reasoning is of primary importance (e.g., HotpotQA), multiple thought-action-observation steps are used for the task-solving trajectory. For decision making tasks involving lots of action steps, thoughts are used sparsely.
Results on Knowledge-Intensive Tasks
The paper first evaluates ReAct on knowledge-intensive reasoning tasks such as question answering (HotPotQA) and fact verification (Fever). PaLM-540B is used as the base model for prompting.
REACT1
Image Source: Yao et al., 2022
The prompting results on HotPotQA and Fever using different prompting methods show that ReAct generally performs better than Act (involves acting only) on both tasks.
We can also observe that ReAct outperforms CoT on Fever and lags behind CoT on HotpotQA. A detailed error analysis is provided in the paper. In summary:
CoT suffers from fact hallucination
ReAct's structural constraint reduces its flexibility in formulating reasoning steps
ReAct depends a lot on the information it's retrieving; non-informative search results derails the model reasoning and leads to difficulty in recovering and reformulating thoughts
Prompting methods that combine and support switching between ReAct and CoT+Self-Consistency generally outperform all the other prompting methods.
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 (text-based game) and WebShop (online shopping website environment). Both involve complex environments that require reasoning to act and explore effectively.
Note that the ReAct prompts are designed differently for these tasks while still keeping the same core idea of combining reasoning and acting. Below is an example for an ALFWorld problem involving ReAct prompting.
REACT2
Image Source: Yao et al., 2022
ReAct outperforms Act on both ALFWorld and Webshop. Act, without any thoughts, fails to correctly decompose goals into subgoals. Reasoning seems to be advantageous in ReAct for these types of tasks but current prompting-based methods are still far from the performance of expert humans on these tasks.
Check out the paper for more detailed results.
LangChain ReAct Usage
Below is a high-level example of how the ReAct prompting approach works in practice. We will be using OpenAI for the LLM and LangChain as it already has built-in functionality that leverages the ReAct framework to build agents that perform tasks by combining the power of LLMs and different tools.
First, let's install and import the necessary libraries:
Colorado orogeny extends into?
Thought 1 I need to search Colorado orogeny, find the area that the eastern sector
of the Colorado orogeny extends into, then find the elevation range of the
area.
Action 1 Search[Colorado orogeny]
Observation 1 The Colorado orogeny was an episode of mountain building (an orogeny) in
Colorado and surrounding areas.
Thought 2 It does not mention the eastern sector. So I need to look up eastern
sector.
Action 2 Lookup[eastern sector]
Observation 2 (Result 1 / 1) The eastern sector extends into the High Plains and is called
the Central Plains orogeny.
Thought 3 The eastern sector of Colorado orogeny extends into the High Plains. So I
need to search High Plains and find its elevation range.
Action 3 Search[High Plains]
Observation 3 High Plains refers to one of two distinct land regions
Thought 4 I need to instead search High Plains (United States).
Action 4 Search[High Plains (United States)]
Observation 4 The High Plains are a subregion of the Great Plains. From east to west, the
High Plains rise in elevation from around 1,800 to 7,000 ft (550 to 2,130
m).[3]
Thought 5 High Plains rise in elevation from around 1,800 to 7,000 ft, so the answer
is 1,800 to 7,000 ft.
Action 5 Finish[1,800 to 7,000 ft]
...
Note that different prompts setups are used for different types of tasks. For tasks where reasoning is of primary importance (e.g., HotpotQA), multiple thought-action-observation steps are used for the task-solving trajectory. For decision making tasks involving lots of action steps, thoughts are used sparsely.
Results on Knowledge-Intensive Tasks
The paper first evaluates ReAct on knowledge-intensive reasoning tasks such as question answering (HotPotQA) and fact verification (Fever). PaLM-540B is used as the base model for prompting.
REACT1
Image Source: Yao et al., 2022
The prompting results on HotPotQA and Fever using different prompting methods show that ReAct generally performs better than Act (involves acting only) on both tasks.
We can also observe that ReAct outperforms CoT on Fever and lags behind CoT on HotpotQA. A detailed error analysis is provided in the paper. In summary:
CoT suffers from fact hallucination
ReAct's structural constraint reduces its flexibility in formulating reasoning steps
ReAct depends a lot on the information it's retrieving; non-informative search results derails the model reasoning and leads to difficulty in recovering and reformulating thoughts
Prompting methods that combine and support switching between ReAct and CoT+Self-Consistency generally outperform all the other prompting methods.
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 (text-based game) and WebShop (online shopping website environment). Both involve complex environments that require reasoning to act and explore effectively.
Note that the ReAct prompts are designed differently for these tasks while still keeping the same core idea of combining reasoning and acting. Below is an example for an ALFWorld problem involving ReAct prompting.
REACT2
Image Source: Yao et al., 2022
ReAct outperforms Act on both ALFWorld and Webshop. Act, without any thoughts, fails to correctly decompose goals into subgoals. Reasoning seems to be advantageous in ReAct for these types of tasks but current prompting-based methods are still far from the performance of expert humans on these tasks.
Check out the paper for more detailed results.
LangChain ReAct Usage
Below is a high-level example of how the ReAct prompting approach works in practice. We will be using OpenAI for the LLM and LangChain as it already has built-in functionality that leverages the ReAct framework to build agents that perform tasks by combining the power of LLMs and different tools.
First, let's install and import the necessary libraries: