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.
66bc84a33ae330ae8bb70232
Kaiser Sun
admin_assigned
2024-08-14T11:09:49.346Z
616745f893f6837511d9ae25
https://cdn-avatars.huggingface.co/v1/production/uploads/616745f893f6837511d9ae25/H0JIGDL5VOu29zVd4KiKB.jpeg
Kaiser Sun
user
KaiserWhoLearns
66bc84a33ae330ae8bb70233
Mark Dredze
admin_assigned
2024-08-14T11:10:26.376Z
6140f23f6dc32b0812420fc5
https://cdn-avatars.huggingface.co/v1/production/uploads/1631646255954-noauth.jpeg
Mark Dredze
user
mdredze
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.
Investigating pre-train-then-fine-tune paradigms reveals that additional pre-training enhances model capabilities and fine-tuning amplifies performance on incapable tasks, but may cause forgetting of prior knowledge and increased sensitivity to evaluation prompts.
pre-training fine-tuning intermediate pre-trained model checkpoints latent improvement dataset capability supervised fine-tuning domain knowledge