The Impact of Data Size on Transformer Training: Overfitting & Loss Dynamics

Table of Links

Abstract and 1 Introduction

2 Related Work

3 Model and 3.1 Associative memories

3.2 Transformer blocks

4 A New Energy Function

4.1 The layered structure

5 Cross-Entropy Loss

6 Empirical Results and 6.1 Empirical evaluation of the radius

6.2 Training GPT-2

6.3 Training Vanilla Transformers

7 Conclusion and Acknowledgments


Appendix A. Deferred Tables

Appendix B. Some Properties of the Energy Functions

Appendix C. Deferred Proofs from Section 5

Appendix D. Transformer Details: Using GPT-2 as an Example


References

6.2 Training GPT-2


Figure 4: Vanilla Transformers trained on the 2M Question-Formation dataset following the settings in (Murty et al., 2023). The training losses stabilize at a value of approximately 1, which corroborates the result presented in Proposition 4.


:::info
Authors:

(1) Xueyan Niu, Theory Laboratory, Central Research Institute, 2012 Laboratories, Huawei Technologies Co., Ltd.;

(2) Bo Bai baibo (8@huawei.com);

(3) Lei Deng (deng.lei2@huawei.com);

(4) Wei Han (harvey.hanwei@huawei.com).

:::


:::info
This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.

:::

Leave a Comment

This site uses Akismet to reduce spam. Learn how your comment data is processed.