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Use of Simpler Patterns in Few-Shot Learning
In the context of few-shot learning, the input-output patterns provided as demonstrations do not need to be complex; it is possible to guide a Large Language Model effectively using simpler pattern structures.
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Ch.3 Prompting - Foundations of Large Language Models
Foundations of Large Language Models
Computing Sciences
Foundations of Large Language Models Course
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Use of Simpler Patterns in Few-Shot Learning
A user provides a language model with the following examples to teach it a new task:
Input: Apple -> Output: AInput: Banana -> Output: BInput: Cherry -> Output: CWhen the user then provides the new input
Input: Grape, the model responds withOutput: G. The user was expecting the output to be the full wordGrape.Which of the following best explains why the model produced an unexpected result?
Constructing an Effective Input-Output Pattern
Predicting Model Behavior from a Few-Shot Pattern
Learn After
Example of a Few-Shot Prompt for Chinese-to-English Translation
A developer is crafting a prompt to have a large language model extract the primary color from a product description. Below are two approaches for the examples (demonstrations) within the prompt:
Approach A: Text: "This t-shirt comes in a vibrant shade of royal blue." Color: The main color is blue. Text: "The car's exterior is painted a deep, glossy black." Color: The main color is black.
Approach B: Text: "This t-shirt comes in a vibrant shade of royal blue." -> blue Text: "The car's exterior is painted a deep, glossy black." -> black
Based on common principles for guiding language models, which approach is generally more effective and why?
Critique and Refine a Few-Shot Prompt
Optimizing a Sentiment Analysis Prompt