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Effect of 'Thinking' Prompts on LLM Performance
The way a prompt is structured can cause significant variations in a Large Language Model's output. Specifically, instructing an LLM to 'think' through a problem can produce completely different and often superior results compared to when the same model is asked to perform the task straightforwardly without being prompted to reason.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Effect of 'Thinking' Prompts on LLM Performance
Chain-of-Thought (COT) Prompting
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Example of a Prompt for Calculating the Mean Square
Improving LLM Reasoning with Step-by-Step Demonstrations
A user is trying to get a Large Language Model (LLM) to solve a complex word problem that involves multiple calculations. Their initial prompt, 'What is the answer to this problem? [Problem text]', results in a quick but incorrect numerical answer. The user then revises the prompt to: 'First, break down the problem into the necessary steps. Then, solve each step, showing your work. Finally, state the final answer. [Problem text]'. This revised prompt leads to a correct solution. Which principle
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Example of a Prompt for Calculating the Average of 2, 4, and 9
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The Mechanism of Reasoning Prompts
Example of a Prompt with Detailed Reasoning Steps
In-Context Learning
Learn After
A user gives a large language model a complex multi-step logic puzzle. They try two different prompts:
Prompt A: "What is the final answer to the following puzzle? [Puzzle text]" Result A: The model provides a single, incorrect answer.
Prompt B: "Think step-by-step to solve the following puzzle. First, break down the problem into smaller parts. Then, reason through each part before providing the final answer. [Puzzle text]" Result B: The model provides a detailed, step-by-step breakdown of its
Improving LLM Output for Financial Analysis
Evaluating Prompt Strategies for Complex Summarization