How to Use Chain-of-Thought (CoT) Prompting for AI

What is Chain-of-Thought Prompting?

Chain-of-thought (CoT) prompting is a technique used to improve the reasoning abilities of LLMs. It involves providing the model with a series of interconnected prompts that simulate a human-like thought process, encouraging it to break down complex problems into smaller, more manageable steps. This approach enhances the model’s ability to solve complex tasks, such as math word problems and commonsense reasoning challenges. This technique was introduced by Google researchers in 2022.

How CoT works?

Traditional prompting methods often involve giving the LLM a single prompt with a question or instruction. However, this approach can be insufficient for complex tasks that require multiple steps of reasoning.

CoT addresses this limitation by providing a sequence of prompts that guide the model through the reasoning process. Each prompt in the chain builds upon the previous one, leading the model closer to the final solution.

CoT prompting explicitly asks the model to generate a step-by-step reasoning process. It helps the model to break down the problem, preventing reasoning failures. CoT is effective because it focuses the attention mechanism of the LLM.

1 Problem Presentation: The model is presented with a prompt that includes a problem requiring reasoning.
2 Guidance to Think Step-by-Step: The prompt encourages the model to articulate its reasoning process through intermediate steps, rather than jumping directly to the final answer.
3 Generation of Intermediate Reasoning Steps: The model generates a series of natural language reasoning steps that lead to the final output, referred to as the “chain of thought”. This could include calculations, deductions, or any other logical steps relevant to the problem.
4 Final Answer Derivation: The model derives the final answer based on the step-by-step reasoning process it has generated.

Example:

Variants of CoT

Demonstrative examples (Few-shot CoT):

• The model is provided with a few examples of problems and their step-by-step solutions to guide its reasoning.
• An example is provided in the above image.

Key difference between CoT and Few-shot prompting:

Few-Shot Prompting:

• Focus: Provides a few examples of input-output pairs to demonstrate the desired task. It shows what the output should look like given a certain input.
• Emphasis: Learning the pattern or mapping between inputs and outputs.
• Example: In a math problem, you might show a few examples of equations and their correct solutions. The model learns to associate the equation structure with the solution format.

Chain-of-Thought Prompting:

• Focus: Provides examples that include the reasoning steps leading to the final output. It shows how to arrive at the output.
• Emphasis: Learning the reasoning process or the “chain of thought” required to solve the problem.
• Example: In the same math problem, you would show not just the equation and the solution, but also the intermediate steps like “add these numbers,” “subtract that number,” etc.

In essence:

• Few-shot prompting teaches by example outputs.
• Chain-of-thought prompting teaches by example reasoning processes.

CoT prompting often builds upon few-shot prompting by adding the crucial element of explicit reasoning steps within the examples. This helps the LLM to not just mimic the output format but to actually understand and replicate the thought process needed to solve the problem.

Explicit Instructions:

• Explicit instructions involve decomposing the problem in the user prompt.
• Use phrases like “First, we need to consider…” to prompt the model.

Example:

Question: What is (3 + 4) * ( 6 / 3 ) + 7?
Instructions: We follow a specific order to solve math problems. First, we do multiplication (*) and division (/). Then, we do addition (+) and subtraction (-). If there are parentheses (like these: ( )), we always do the calculations inside them first. 
Answer:

Implicit Instructions (Zero-shot-CoT):

• Implicit instructions use the phrase “Let’s think step by step”.
• This prompts the model to reason aloud through all the required steps.

Example:

Benefits of Chain-of-Thought Prompting

• Improved accuracy: The model can handle complex tasks more accurately.
• Enhanced interpretability: The step-by-step explanations make the reasoning process transparent.
• Generalization to new tasks: The model can generalize its reasoning abilities to new, unseen tasks.

Limitations and Considerations

• Model dependency: CoT effectiveness depends on the capabilities of the LLM. Large models like GPT-3 and GPT-4 are more suitable for CoT.
• Prompt generation: Crafting effective CoT prompts can be challenging. (how to measure effectiveness of prompt)
• Performance: CoT might not be effective for all tasks.
• Verbose output: CoT can result in longer and more verbose outputs.