Learn Before
Implications of the Likelihood Maximization Objective
When a large language model is trained with the objective to 'maximize the likelihood of the training data,' what does this objective functionally compel the model to learn about the patterns and structures within that text? Furthermore, explain how this learned knowledge translates into the model's ability to generate coherent and probable sequences of words.
0
1
Tags
Ch.2 Generative Models - Foundations of Large Language Models
Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Analysis in Bloom's Taxonomy
Cognitive Psychology
Psychology
Social Science
Empirical Science
Science
Related
Challenges of Scaling LLM Training
An AI development team is training a new language model on a large corpus of text. Their training algorithm repeatedly adjusts the model's internal parameters. The primary goal of these adjustments is to increase the model's ability to assign a high probability to the sequences of words that actually appear in the training corpus. Which fundamental principle of model training does this process exemplify?
Evaluating LLM Training Objectives
Implications of the Likelihood Maximization Objective