Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.15478 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909301094744064 |
|---|---|
| author | Nath, Swaroop Khadilkar, Harshad Bhattacharyya, Pushpak |
| author_facet | Nath, Swaroop Khadilkar, Harshad Bhattacharyya, Pushpak |
| contents | Transformers have become pivotal in Natural Language Processing, demonstrating remarkable success in applications like Machine Translation and Summarization. Given their widespread adoption, several works have attempted to analyze the expressivity of Transformers. Expressivity of a neural network is the class of functions it can approximate. A neural network is fully expressive if it can act as a universal function approximator. We attempt to analyze the same for Transformers. Contrary to existing claims, our findings reveal that Transformers struggle to reliably approximate smooth functions, relying on piecewise constant approximations with sizable intervals. The central question emerges as: ''Are Transformers truly Universal Function Approximators?'' To address this, we conduct a thorough investigation, providing theoretical insights and supporting evidence through experiments. Theoretically, we prove that Transformer Encoders cannot approximate smooth functions. Experimentally, we complement our theory and show that the full Transformer architecture cannot approximate smooth functions. By shedding light on these challenges, we advocate a refined understanding of Transformers' capabilities. Code Link: https://github.com/swaroop-nath/transformer-expressivity. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_15478 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Transformers are Expressive, But Are They Expressive Enough for Regression? Nath, Swaroop Khadilkar, Harshad Bhattacharyya, Pushpak Machine Learning Transformers have become pivotal in Natural Language Processing, demonstrating remarkable success in applications like Machine Translation and Summarization. Given their widespread adoption, several works have attempted to analyze the expressivity of Transformers. Expressivity of a neural network is the class of functions it can approximate. A neural network is fully expressive if it can act as a universal function approximator. We attempt to analyze the same for Transformers. Contrary to existing claims, our findings reveal that Transformers struggle to reliably approximate smooth functions, relying on piecewise constant approximations with sizable intervals. The central question emerges as: ''Are Transformers truly Universal Function Approximators?'' To address this, we conduct a thorough investigation, providing theoretical insights and supporting evidence through experiments. Theoretically, we prove that Transformer Encoders cannot approximate smooth functions. Experimentally, we complement our theory and show that the full Transformer architecture cannot approximate smooth functions. By shedding light on these challenges, we advocate a refined understanding of Transformers' capabilities. Code Link: https://github.com/swaroop-nath/transformer-expressivity. |
| title | Transformers are Expressive, But Are They Expressive Enough for Regression? |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2402.15478 |