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Data Scale Disparity: Pre-training vs. Fine-tuning
A fundamental distinction between pre-training and fine-tuning lies in the scale of data required. While more fine-tuning data is generally beneficial, the amount needed is orders of magnitude smaller than what is required for pre-training. For instance, fine-tuning can be effectively performed with tens or hundreds of thousands of samples, or even fewer if the data is of high quality. In contrast, pre-training models demands billions or even trillions of tokens, which consequently results in significantly larger computational requirements and longer training times.
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Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
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Types of Pretrained Language Model
Pre-training tasks
Extensions of Pre-trained models
Foundation Models
Historical Context of Pre-training
Examples of Pre-trained Transformers by Architecture
Paradigm Shift in NLP Driven by Pre-training
Future Research Directions in Large-Scale Pre-training
Role of Pre-training in Developing Latent Abilities
Common Data Sources for Pre-training LLMs
Training Auxiliary Parameters with a Fixed Transformer Model
Synergy of Transformers and Self-Supervised Learning
Core Problem Types in NLP Pre-training
Scope of Introductory Discussions on Pre-training
Application of Self-Supervised Pre-training Across Model Architectures
Scope of Foundational Concepts in Pre-training and Adaptation
Tokens vs. Words in NLP
Self-supervised Pre-training
Data Scale Disparity: Pre-training vs. Fine-tuning
A small biotech company wants to build an AI model to classify protein sequences for a very specific function. They have a high-quality, but small, labeled dataset of 10,000 sequences. They have limited computational resources and a tight deadline. Which of the following strategies represents the most effective and efficient approach for them to develop a high-performing model?
Diagnosing a Flawed Model Development Strategy
The development of large-scale AI models typically involves two distinct stages. Match each characteristic below to the stage it describes.
Scope of Introductory Discussion on Pre-training in NLP
Learn After
Evaluating a Data Strategy for a Specialized AI Model
A small startup is building a specialized legal document analysis tool. They plan to adapt a large, general-purpose pre-trained language model for this task. Given their limited budget and resources, which of the following data strategies is most likely to lead to a successful and efficient outcome?
Evaluating Datasets for Model Adaptation