Enabling Instruction Following via Pre-training
One method to equip a Large Language Model with the ability to follow instructions is to include training samples that pair instructions with their correct responses directly into the model's pre-training dataset.
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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
Ch.2 Generative Models - Foundations of Large Language Models
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Acquiring Instruction Knowledge During Pre-training
A developer is using a pre-trained language model for a new task: converting a user's informal description of a meeting into a structured JSON object with 'title', 'date', and 'attendees' keys. Which of the following textual instructions, provided to the model along with the user's description, would be most effective at consistently producing the correct output format?
Enabling Instruction Following via Pre-training
Choosing Appropriate Instruction Formats
Diagnosing and Refining Task Instructions
Universal Language Framework via Textual Inputs
A developer is building a tool that allows users to generate custom email responses by providing specific commands, such as 'Write a polite refusal to this meeting invitation.' They have two language models to choose from:
- Model X: A very large model trained on a vast library of books and websites, excelling at generating fluent, human-like text.
- Model Y: A smaller model specifically trained on a dataset of commands paired with their correct outputs.
Which model is the more suitable choice for this tool, and why?
Enabling Instruction Following via Pre-training
Diagnosing a Language Model's Output
Analyzing Model Failure
Learn After
Enabling Zero-Shot Learning through Instruction Understanding
Computational Expense of Training LLMs from Scratch
Difficulty in Collecting Labeled Data for Instruction Pre-training
A research lab develops a new large language model by training it on a massive dataset consisting solely of digitized books and encyclopedias. The model becomes exceptionally proficient at generating coherent, factual paragraphs. However, when users give it a direct command, such as "Translate 'hello' into French," the model often responds with a continuation like "is a common English greeting," instead of "Bonjour."
Which of the following best analyzes the most likely reason for this specific failure?
Pre-training Data Strategy for a Command-Following Model
Pre-training a Specialized Code Assistant