Effectiveness of Large and Diverse Pre-training for Out-of-Distribution Generalization
Empirical evidence demonstrates that pre-training models on large-scale, diverse datasets is a successful method for improving their ability to generalize to out-of-distribution data.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Learn After
Reduced Necessity of Fine-Tuning for Generalization with Extensive Pre-training
AI Model Training Strategy for Generalization
A research lab is developing a new language model. Their primary goal is to create a model that can reliably handle tasks and data types it was not explicitly trained on, such as analyzing niche scientific papers and summarizing newly emerging slang on social media. They are considering two main training strategies:
Strategy A: Curate a massive, diverse dataset from a wide range of sources (books, web pages, code, academic articles, social media) and use the majority of their computational budg
Evaluating Pre-training Strategies for Specialized AI