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