Amuro and Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models
Abstract: 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, we find 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 supervised fine-tuned model resembles high sensitivity to few-shot evaluation prompts, but this sensitivity can be alleviated by more pre-training.
Paper Type: Long
Research Area: Language Modeling
Research Area Keywords: Fine-tuning, Pre-training, Instruction Tuning, Training Dynamics
Contribution Types: Model analysis & interpretability
Languages Studied: English
Submission Number: 4449
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