Amuro & Char: Analyzing the Relationship between Pre-Training and
  Fine-Tuning of Large Language Models
Authors: name: Kaiser Sun, name: Mark Dredze

The development of large language models leads to the formation of a
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. In this work, we investigate the relationship
between pre-training and fine-tuning by fine-tuning multiple intermediate
pre-trained model checkpoints. Our results on 18 datasets suggest that i)
continual pre-training improves the model in a latent way that unveils after
fine-tuning; ii) with extra fine-tuning, the datasets that the model does not
demonstrate capability gain much more than those that the model performs well
during the pre-training stage; iii) although model benefits significantly
through supervised fine-tuning, it may forget previously known domain knowledge
and the tasks that are not seen during fine-tuning; iv) the model resembles
high sensitivity to evaluation prompts after supervised fine-tuning, but this
sensitivity can be alleviated by more pre-training.