Learn Before
Optimizing for Generalizability in Pre-training
Optimizing a neural network's parameters, denoted as , during a pre-training task is a fundamental challenge. Unlike standard learning problems in Natural Language Processing (NLP), pre-training does not assume specific downstream tasks to which the model will be applied. Instead, the primary goal is to train a model that can generalize across various tasks.
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Ch.1 Pre-training - Foundations of Large Language Models
Foundations of Large Language Models Course
Computing Sciences
Foundations of Large Language Models
Learn After
A research team is pre-training a large language model. They observe that the model's loss on the pre-training objective is still decreasing, indicating better performance on that specific task. However, when they periodically evaluate the model on a diverse suite of benchmark tasks it has not been trained on, its performance on those tasks has started to decline. What does this scenario most strongly suggest about the training process in relation to its primary goal?
Evaluating Pre-training Strategies for Generalizability
In the context of pre-training a large language model, the primary and ultimate measure of success is achieving the lowest possible value for the loss function on the pre-training task.