Differentiating Training Objectives in Language Models
A language model is first trained on a vast corpus of internet text to learn general language patterns. It is then further trained on a curated dataset of prompts and their desired responses to become a helpful assistant. Describe the primary difference in the mathematical objective being optimized during these two distinct training phases.
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Ch.4 Alignment - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
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A language model undergoes two distinct training stages. In the first stage, it is trained on a massive, unlabeled dataset of books and websites with the goal of learning to predict the next word in any given sentence. In the second stage, it is trained on a smaller, curated dataset of user prompts paired with ideal answers, with the goal of learning to generate helpful responses to the prompts. Which statement best analyzes the fundamental shift in the model's training objective between these two stages?
Differentiating Training Objectives in Language Models
During instruction fine-tuning, the model's training objective is to maximize the probability of the entire input-output sequence, treating the user's instruction and the desired response as a single, continuous piece of text.