On the Importance of Data Size in Probing Fine-tuned Models
Houman Mehrafarin, Sara Rajaee, Mohammad Taher Pilehvar
Abstract
Several studies have investigated the reasons behind the effectiveness of fine-tuning, usually through the lens of probing. However, these studies often neglect the role of the size of the dataset on which the model is fine-tuned. In this paper, we highlight the importance of this factor and its undeniable role in probing performance. We show that the extent of encoded linguistic knowledge depends on the number of fine-tuning samples. The analysis also reveals that larger training data mainly affects higher layers, and that the extent of this change is a factor of the number of iterations updating the model during fine-tuning rather than the diversity of the training samples. Finally, we show through a set of experiments that fine-tuning data size affects the recoverability of the changes made to the model’s linguistic knowledge.
BibTeX
@inproceedings{mehrafarin-etal-2022-importance,
title = "On the Importance of Data Size in Probing Fine-tuned Models",
author = "Mehrafarin, Houman and
Rajaee, Sara and
Pilehvar, Mohammad Taher",
editor = "Muresan, Smaranda and
Nakov, Preslav and
Villavicencio, Aline",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2022",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.findings-acl.20/",
doi = "10.18653/v1/2022.findings-acl.20",
pages = "228--238"
}