Abstract
Pretrained embeddings based on the Transformer architecture have taken the NLP community by storm. We show that they can mathematically be reframed as a sum of vector factors and showcase how to use this reframing to study the impact of each component. We provide evidence that multi-head attentions and feed-forwards are not equally useful in all downstream applications, as well as a quantitative overview of the effects of finetuning on the overall embedding space. This approach allows us to draw connections to a wide range of previous studies, from vector space anisotropy to attention weights.- Anthology ID:
- 2022.tacl-1.57
- Volume:
- Transactions of the Association for Computational Linguistics, Volume 10
- Month:
- Year:
- 2022
- Address:
- Cambridge, MA
- Venue:
- TACL
- SIG:
- Publisher:
- MIT Press
- Note:
- Pages:
- 981–996
- Language:
- URL:
- https://aclanthology.org/2022.tacl-1.57
- DOI:
- 10.1162/tacl_a_00501
- Cite (ACL):
- Timothee Mickus, Denis Paperno, and Mathieu Constant. 2022. How to Dissect a Muppet: The Structure of Transformer Embedding Spaces. Transactions of the Association for Computational Linguistics, 10:981–996.
- Cite (Informal):
- How to Dissect a Muppet: The Structure of Transformer Embedding Spaces (Mickus et al., TACL 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.tacl-1.57.pdf