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

Example of a Prompt for Calculating the Average of 1, 3, 5, and 7

Insufficiency of Simple Demonstrations for LLM Reasoning Tasks

A user wants a language model to extract the main sentiment from a sentence and express it as a single emoji. The initial prompt, 'What is the sentiment of this sentence, as an emoji: The movie was a spectacular triumph!', results in the model responding with text like 'The sentiment is positive.' Which of the following revised prompts is structured to be the most effective at teaching the model the desired task?

Improving LLM Classification Consistency

Crafting an Effective Prompt with Demonstrations

Example of a Few-Shot Prompt for Polarity Classification