Nonparametric Variational Regularisation of Pretrained Transformers
Fabio Fehr James Henderson
The current paradigm of large-scale pre-training and fine-tuning Transformer
large language models has lead to significant improvements across the board in
natural language processing. However, such large models are susceptible to
overfitting to their training data, and as a result the models perform poorly
when the domain changes. Also, due to the model's scale, the cost of
fine-tuning the model to the new domain is large. Nonparametric Variational
Information Bottleneck (NVIB) has been proposed as a regulariser for training
cross-attention in Transformers, potentially addressing the overfitting
problem. We extend the NVIB framework to replace all types of attention
functions in Transformers, and show that existing pretrained Transformers can
be reinterpreted as Nonparametric Variational (NV) models using a proposed
identity initialisation. We then show that changing the initialisation
introduces a novel, information-theoretic post-training regularisation in the
attention mechanism, which improves out-of-domain generalisation without any
training. This success supports the hypothesis that pretrained Transformers are
implicitly NV Bayesian models.