Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models
<|reference_start|>Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models: 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.<|reference_end|>
@article{sun2024amuro,
    title={Amuro & Char: Analyzing the Relationship between Pre-Training and
  Fine-Tuning of Large Language Models},
    author={Kaiser Sun, Mark Dredze},
    journal={arXiv preprint arXiv:2408.06663},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.06663},
    primaryClass={cs.CL cs.AI}
}