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

• URL: http://arxiv.org/abs/2408.06663v2
• Date: Wed, 14 Aug 2024 15:23:38 GMT
• Title: Amuro & Char: Analyzing the Relationship between Pre-Training and Fine-Tuning of Large Language Models
• Authors: Kaiser Sun, Mark Dredze,
• Abstract summary: 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 pre-training improves the model in a latent way that unveils after fine-tuning.
• Score: 17.288865972774587
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

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