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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
Multi-Round Interaction to Guide LLM Reasoning
Example of a Prompt for a Direct Mathematical Calculation
Example of a Prompt for Calculating the Average of 1, 3, 5, and 7
Example of a Prompt for Calculating the Mean Square
Improving LLM Reasoning with Step-by-Step Demonstrations
In-Context Learning (ICL)
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 of interacting with LLMs does this scenario best illustrate?
Evaluating Prompt Strategies for a Logic Puzzle
Prompting for a Reasoning Process to Mitigate Errors in Complex Tasks
Example of a Prompt for Calculating the Average of 2, 4, and 9
Improving LLM Problem-Solving by Demonstrating Reasoning Steps
The Mechanism of Reasoning Prompts
Example of a Prompt with Detailed Reasoning Steps
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 reasoning, arriving at the correct final answer.
Based on these results, what is the most accurate explanation for the difference in the model's performance?
Improving LLM Output for Financial Analysis
Evaluating Prompt Strategies for Complex Summarization