Creating a CoT Prompt by Incorporating Reasoning Steps
In Chain-of-Thought (CoT) prompting, large language models benefit significantly more from demonstrations that include detailed problem-solving steps than from simple question-answer pairs. The fundamental technique for creating a CoT prompt is to incorporate these intermediate reasoning steps, which allows the model to learn the process for deriving an answer, not just the final answer itself.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Ch.3 Prompting - Foundations of Large Language Models
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A developer is trying to improve a language model's ability to solve multi-step word problems. They compare two prompting strategies.
Strategy 1: Provide the model with a new word problem and ask for the final answer directly.
Strategy 2: Provide the model with a new word problem, but first show it an example of a similar problem where the solution is explicitly broken down into logical, sequential steps before reaching the final conclusion.
Why is Strategy 2 generally more effective for improving the model's reasoning on complex tasks?
Improving a Prompt for a Multi-Step Problem
Few-Shot Chain-of-Thought (CoT) Prompting
Practical Limitations of Chain-of-Thought Prompting
The primary benefit of a prompting technique that demonstrates a step-by-step reasoning process is that it permanently modifies the language model's internal weights, making it inherently better at solving similar problems in the future, even without the detailed prompt.
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Example of One-Shot Chain-of-Thought (COT) Prompting
Problem-Solving Scenarios for Chain-of-Thought Prompting
Self-Consistency Method
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Creating a CoT Prompt by Incorporating Reasoning Steps
Diagnosing a Flawed Prompting Strategy
A developer is trying to get a large language model to solve two-step arithmetic word problems. They use a few-shot prompting strategy, providing several examples. Each example consists of a word problem followed only by its final numerical answer (e.g., 'Problem: ... Answer: 15'). The model consistently fails to solve new, slightly different word problems. What is the most likely reason for the model's poor performance?
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Learn After
A developer is creating a prompt to help a large language model solve multi-step word problems. The goal is to structure the prompt in a way that teaches the model how to reason through the problem before providing a final answer. Analyze the following prompt structures and select the one that best demonstrates the technique of including intermediate reasoning steps to guide the model's problem-solving process.
Problem to solve: 'A farmer has 15 apples. He sells 5 to his neighbor and then buys 10 more from the market. How many apples does he have now?'
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