Abstract
The question of what kinds of linguistic information are encoded in different layers of Transformer-based language models is of considerable interest for the NLP community. Existing work, however, has overwhelmingly focused on word-level representations and encoder-only language models with the masked-token training objective. In this paper, we present experiments with semantic structural probing, a method for studying sentence-level representations via finding a subspace of the embedding space that provides suitable task-specific pairwise distances between data-points. We apply our method to language models from different families (encoder-only, decoder-only, encoder-decoder) and of different sizes in the context of two tasks, semantic textual similarity and natural-language inference. We find that model families differ substantially in their performance and layer dynamics, but that the results are largely model-size invariant.- Anthology ID:
- 2023.blackboxnlp-1.11
- Volume:
- Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Yonatan Belinkov, Sophie Hao, Jaap Jumelet, Najoung Kim, Arya McCarthy, Hosein Mohebbi
- Venues:
- BlackboxNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 142–154
- Language:
- URL:
- https://aclanthology.org/2023.blackboxnlp-1.11
- DOI:
- 10.18653/v1/2023.blackboxnlp-1.11
- Cite (ACL):
- Dmitry Nikolaev and Sebastian Padó. 2023. Investigating Semantic Subspaces of Transformer Sentence Embeddings through Linear Structural Probing. In Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, pages 142–154, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Investigating Semantic Subspaces of Transformer Sentence Embeddings through Linear Structural Probing (Nikolaev & Padó, BlackboxNLP-WS 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.blackboxnlp-1.11.pdf