Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2410.13802 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917806988066816 |
|---|---|
| author | Zavoral, Patrik Variš, Dušan Bojar, Ondřej |
| author_facet | Zavoral, Patrik Variš, Dušan Bojar, Ondřej |
| contents | The Transformer model has a tendency to overfit various aspects of the training data, such as the overall sequence length. We study elementary string edit functions using a defined set of error indicators to interpret the behaviour of the sequence-to-sequence Transformer. We show that generalization to shorter sequences is often possible, but confirm that longer sequences are highly problematic, although partially correct answers are often obtained. Additionally, we find that other structural characteristics of the sequences, such as subsegment length, may be equally important. We hypothesize that the models learn algorithmic aspects of the tasks simultaneously with structural aspects but adhering to the structural aspects is unfortunately often preferred by Transformer when they come into conflict. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_13802 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Adversarial Testing as a Tool for Interpretability: Length-based Overfitting of Elementary Functions in Transformers Zavoral, Patrik Variš, Dušan Bojar, Ondřej Machine Learning The Transformer model has a tendency to overfit various aspects of the training data, such as the overall sequence length. We study elementary string edit functions using a defined set of error indicators to interpret the behaviour of the sequence-to-sequence Transformer. We show that generalization to shorter sequences is often possible, but confirm that longer sequences are highly problematic, although partially correct answers are often obtained. Additionally, we find that other structural characteristics of the sequences, such as subsegment length, may be equally important. We hypothesize that the models learn algorithmic aspects of the tasks simultaneously with structural aspects but adhering to the structural aspects is unfortunately often preferred by Transformer when they come into conflict. |
| title | Adversarial Testing as a Tool for Interpretability: Length-based Overfitting of Elementary Functions in Transformers |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2410.13802 |