Enregistré dans:
| Auteurs principaux: | , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2023
|
| Sujets: | |
| Accès en ligne: | https://arxiv.org/abs/2301.11688 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866910645642854400 |
|---|---|
| author | Hościłowicz, Jakub Sowański, Marcin Czubowski, Piotr Janicki, Artur |
| author_facet | Hościłowicz, Jakub Sowański, Marcin Czubowski, Piotr Janicki, Artur |
| contents | In this article, we use probing to investigate phenomena that occur during fine-tuning and knowledge distillation of a BERT-based natural language understanding (NLU) model. Our ultimate purpose was to use probing to better understand practical production problems and consequently to build better NLU models. We designed experiments to see how fine-tuning changes the linguistic capabilities of BERT, what the optimal size of the fine-tuning dataset is, and what amount of information is contained in a distilled NLU based on a tiny Transformer. The results of the experiments show that the probing paradigm in its current form is not well suited to answer such questions. Structural, Edge and Conditional probes do not take into account how easy it is to decode probed information. Consequently, we conclude that quantification of information decodability is critical for many practical applications of the probing paradigm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2301_11688 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU? Hościłowicz, Jakub Sowański, Marcin Czubowski, Piotr Janicki, Artur Computation and Language Machine Learning In this article, we use probing to investigate phenomena that occur during fine-tuning and knowledge distillation of a BERT-based natural language understanding (NLU) model. Our ultimate purpose was to use probing to better understand practical production problems and consequently to build better NLU models. We designed experiments to see how fine-tuning changes the linguistic capabilities of BERT, what the optimal size of the fine-tuning dataset is, and what amount of information is contained in a distilled NLU based on a tiny Transformer. The results of the experiments show that the probing paradigm in its current form is not well suited to answer such questions. Structural, Edge and Conditional probes do not take into account how easy it is to decode probed information. Consequently, we conclude that quantification of information decodability is critical for many practical applications of the probing paradigm. |
| title | Can We Use Probing to Better Understand Fine-tuning and Knowledge Distillation of the BERT NLU? |
| topic | Computation and Language Machine Learning |
| url | https://arxiv.org/abs/2301.11688 |