Learn Before
Demonstrations in In-Context Learning
In the context of few-shot learning, demonstrations are example input-output pairs included in a prompt. These samples serve to teach a large language model how to perform a specific task by showing it concrete examples of the desired behavior.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Example of In-Context Learning for Sentiment Classification
Example of an Instructional Prompt in a Few-Shot Setting for Sub-Problem Decomposition
Troubleshooting a Prompting Strategy
Demonstrations in In-Context Learning
A developer wants a language model to consistently translate informal text messages into a formal, professional tone. The goal is to guide the model's output by showing it examples of the desired transformation directly within the query, without altering the model's permanent parameters. Which of the following inputs best applies this in-context learning method?
Analyzing a Prompt's Structure for In-Context Task Learning
A developer is constructing a prompt to teach a language model a new task by providing examples directly in the input. Match each component of the prompt to its specific role in this in-context learning process.
Failure of Standard Few-Shot Prompting for Average Calculation
Learn After
A developer is trying to get a language model to extract product codes from customer emails. They provide the following examples in the prompt before asking the model to process a new email:
Example 1: Input: 'Hi, my SuperWidget model SW-1000 is broken.' Output: 'SW-1000'
Example 2: Input: 'I need a replacement part for my SuperWidget Pro, model number SW-2500.' Output: 'SW-2500'
New Email: Input: 'My GigaGadget GG-500 won't turn on.'
The model incorrectly outputs 'SW-500'. Based on an analysis of the provided examples, what is the most likely reason for this error?
Evaluating Prompt Demonstrations
Evaluating and Improving Prompt Demonstrations
Learning Output Formatting from Demonstrations