Life after BERT: What do Other Muppets Understand about Language?

• URL: http://arxiv.org/abs/2205.10696v1
• Date: Sat, 21 May 2022 23:57:17 GMT
• Title: Life after BERT: What do Other Muppets Understand about Language?
• Authors: Vladislav Lialin, Kevin Zhao, Namrata Shivagunde, Anna Rumshisky
• Abstract summary: We use oLMpics benchmark and psycholinguistic probing datasets for a diverse set of 29 models. We adapt the oLMpics zero-shot setup for autoregressive models and evaluate GPT networks of different sizes.
• Score: 7.896970044689526
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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