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