How does the pre-training objective affect what large language models learn about linguistic properties?
This work questions the dominant narrative in NLP about the benefits of linguistically informed pre-training for language models, potentially impacting researchers and practitioners in the field.
The study investigated how different pre-training objectives affect BERT's learning of linguistic properties, finding only small differences in probing performance between linguistically motivated and non-linguistically motivated objectives, which challenges the idea that linguistically informed pre-training leads to better linguistic knowledge.
Several pre-training objectives, such as masked language modeling (MLM), have been proposed to pre-train language models (e.g. BERT) with the aim of learning better language representations. However, to the best of our knowledge, no previous work so far has investigated how different pre-training objectives affect what BERT learns about linguistics properties. We hypothesize that linguistically motivated objectives such as MLM should help BERT to acquire better linguistic knowledge compared to other non-linguistically motivated objectives that are not intuitive or hard for humans to guess the association between the input and the label to be predicted. To this end, we pre-train BERT with two linguistically motivated objectives and three non-linguistically motivated ones. We then probe for linguistic characteristics encoded in the representation of the resulting models. We find strong evidence that there are only small differences in probing performance between the representations learned by the two different types of objectives. These surprising results question the dominant narrative of linguistically informed pre-training.