  Contrastive learning has been demonstrated to be effective in enhancing
pre-trained language models (PLMs) to derive superior universal sentence
embeddings. However, existing contrastive methods still have two limitations.
Firstly, previous works may acquire poor performance under domain shift
settings, thus hindering the application of sentence representations in
practice. We attribute this low performance to the over-parameterization of
PLMs with millions of parameters. To alleviate it, we propose PromCSE
(Prompt-based Contrastive Learning for Sentence Embeddings), which only trains
small-scale \emph{Soft Prompt} (i.e., a set of trainable vectors) while keeping
PLMs fixed. Secondly, the commonly used NT-Xent loss function of contrastive
learning does not fully exploit hard negatives in supervised learning settings.
To this end, we propose to integrate an Energy-based Hinge loss to enhance the
pairwise discriminative power, inspired by the connection between the NT-Xent
loss and the Energy-based Learning paradigm. Empirical results on seven
standard semantic textual similarity (STS) tasks and a domain-shifted STS task
both show the effectiveness of our method compared with the current
state-of-the-art sentence embedding models. Our code is publicly avaliable at
https://github.com/YJiangcm/PromCSE
