Life after BERT: What do Other Muppets Understand about Language?
Vladislav Lialin, Kevin Zhao, Namrata Shivagunde, Anna Rumshisky · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · 2022
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 benchmark 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 autoregressive 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.The code for this study is available on GitHub 1 .