Learn Before
Alternative Phrases for Triggering Chain-of-Thought Reasoning
To elicit a reasoning process from a Large Language Model, various instructional phrases can be used as alternatives to the common 'Let’s think step by step.' Examples of such trigger phrases include 'Let’s think logically' and 'Please show me your thinking steps first,' which also prompt the model to articulate its problem-solving process.
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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
Related
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Alternative Phrases for Triggering Chain-of-Thought Reasoning
A user wants a large language model to solve a multi-step word problem. The model's initial attempts provide only a final, incorrect answer. The user's goal is to modify the prompt to encourage the model to generate a detailed, step-by-step thought process first, which should lead to a more accurate final answer. Crucially, the user does not want to include a complete, solved example of another problem in the prompt. Which of the following prompt modifications best achieves this specific goal?
To successfully prompt a language model to generate a step-by-step thought process for a new problem, one must always include a complete, solved example of a similar problem within the prompt.
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Choosing and Justifying a Prompting Strategy Under Context and Quality Constraints
Designing a Prompting Workflow for a High-Stakes, Multi-Step Task
Diagnosing and Redesigning a Prompting Approach for a Decomposed Workflow
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Zero-Shot CoT Example with Jack's Apples
Learn After
A user is trying to get a language model to solve a multi-step logic puzzle. The initial prompt is simply 'Solve this puzzle: [puzzle details]'. The model consistently provides an incorrect final answer without explaining how it arrived at it. Which of the following revised prompts is most likely to improve the model's ability to reach the correct solution?
Improving LLM Prompt for Complex Task
A prompt engineer needs to elicit a specific style of reasoning from a Large Language Model for different tasks. Match each task with the instructional phrase best suited to trigger the most appropriate and effective thought process.