polish the paper NAME_1.

We propose a simple and efficient pretraining framework, NAME_2, that unifies the language modeling and contrastive learning objectives in one training step. Based on the examples of similar text pairs chosen by their Longest Common Substring (LCS) from raw unlabeled documents, our pretraining scheme consists of masked language modeling combined with an unsupervised contrastive learning task. The resulting model, NAME_2, performs surprisingly well without any labeled training data points. Specifically, it significantly outperforms all other pretrained baselines as a retriever at the recall benchmark in a zero-shot setting and is competitive with BM25, a strong sparse baseline. Of course, NAME_2 keeps the ability of language modeling in our ablation experiment. We release our code at \url{https://github.com/NAME_3/NAME_2}.

