  Language models with the Transformers structure have shown great performance
in natural language processing. However, there still poses problems when
fine-tuning pre-trained language models on downstream tasks, such as
over-fitting or representation collapse. In this work, we propose HyPe, a
simple yet effective fine-tuning technique to alleviate such problems by
perturbing hidden representations of Transformers layers. Unlike previous works
that only add noise to inputs or parameters, we argue that the hidden
representations of Transformers layers convey more diverse and meaningful
language information. Therefore, making the Transformers layers more robust to
hidden representation perturbations can further benefit the fine-tuning of PLMs
en bloc. We conduct extensive experiments and analyses on GLUE and other
natural language inference datasets. Results demonstrate that HyPe outperforms
vanilla fine-tuning and enhances generalization of hidden representations from
different layers. In addition, HyPe acquires negligible computational
overheads, and is better than and compatible with previous state-of-the-art
fine-tuning techniques.
