Learn Before
Components of a Prompt: Instruction and User Input
In many common applications, a prompt is structured with two main parts: an instruction that specifies the task to be performed, and the user's input to which the task should be applied. The Large Language Model is then expected to follow the given instruction to correctly process the input.
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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
Ch.4 Alignment - Foundations of Large Language Models
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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
Learn After
Tuple Representation of a Simple Prompt
A user wants to generate a one-paragraph summary of a long article. They provide the entire article text to a language model as the prompt, but the model's output is a continuation of the article's topic rather than a summary. Based on the principles of effective prompt structure, what is the most likely reason for this poor result?
Analyzing a Flawed Prompt
Deconstructing a Prompt