Life after BERT: What do Other Muppets Understand about Language?
Vladislav Lialin, Kevin Zhao, Namrata Shivagunde, Anna Rumshisky
Abstract
Existing pre-trained transformer analysis works usually focus only on one or two model families at a time, overlooking the variability of the architecture and pre-training objectives. In our work, we utilize the oLMpics bench- mark and psycholinguistic probing datasets for a diverse set of 29 models including T5, BART, and ALBERT. Additionally, we adapt the oLMpics zero-shot setup for autoregres- sive models and evaluate GPT networks of different sizes. Our findings show that none of these models can resolve compositional questions in a zero-shot fashion, suggesting that this skill is not learnable using existing pre-training objectives. Furthermore, we find that global model decisions such as architecture, directionality, size of the dataset, and pre-training objective are not predictive of a model’s linguistic capabilities.- Anthology ID:
- 2022.acl-long.227
- Volume:
- Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Venue:
- ACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3180–3193
- Language:
- URL:
- https://aclanthology.org/2022.acl-long.227
- DOI:
- 10.18653/v1/2022.acl-long.227
- Cite (ACL):
- Vladislav Lialin, Kevin Zhao, Namrata Shivagunde, and Anna Rumshisky. 2022. Life after BERT: What do Other Muppets Understand about Language?. In Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 3180–3193, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- Life after BERT: What do Other Muppets Understand about Language? (Lialin et al., ACL 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.acl-long.227.pdf
- Code
- kev-zhao/life-after-bert
- Data
- WebText