Amuro and Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models

Kaiser Sun, Mark Dredze

Keywords: Fine-tuning, Pre-training, Instruction Tuning, Training Dynamics

Abstract: Large language model development relies on the pre-train-then-align paradigm, in which the model is typically pre-trained on a large text corpus and undergoes a tuning stage to align the model with human preference or downstream tasks. We investigate the relationship between pre-training and fine-tuning by fine-tuning multiple intermediate pre-trained model checkpoints to understand how models develop as they train. Our results on 18 datasets suggest that i) continual pre-training improves the model in a latent way that manifests after fine-tuning; ii) fine-tuning most benefits datasets where the model does not show capability during pre-training; iii) although the model benefits significantly through supervised fine-tuning, it may forget previously known domain knowledge and tasks not seen during fine-tuning; iv) the model exhibits high sensitivity to evaluation prompts after supervised fine-tuning, but this sensitivity can be alleviated through more pre-training.

Primary Area: foundation or frontier models, including LLMs

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Submission Number: 4649