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Choosing a Model Adaptation Strategy
A startup has access to a large, general-purpose language model and wants to quickly launch a new feature that generates creative marketing slogans. They are considering two strategies. Based on the principles of guiding a pre-trained model's output without altering its underlying structure, which strategy should they choose and why?
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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
Application in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
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Zero/Few-Shot Learning
A team is tasked with adapting a large, pre-trained language model to summarize legal documents. One developer designs a method where each summarization request includes a detailed set of instructions and examples of high-quality summaries, which are provided to the original, unchanged model. Another developer uses a large dataset of legal documents and their corresponding summaries to make small, permanent adjustments to the model's internal configuration before deploying it. What is the most significant difference between these two approaches regarding the pre-trained model itself?
Choosing a Model Adaptation Strategy
Key Areas of Prompt Engineering
Instruction-Following Ability of LLMs
Components of a Prompt: Instruction and User Input
When a language model successfully performs a new task based on a well-crafted prompt, its internal parameters are temporarily adjusted for the duration of that specific task to better align with the provided instructions.
Prompting as a Text Generation Task