Generate to Understand for Representation
Changshang Xue Xiande Zhong Xiaoqing Liu
In recent years, a significant number of high-quality pretrained models have
emerged, greatly impacting Natural Language Understanding (NLU), Natural
Language Generation (NLG), and Text Representation tasks. Traditionally, these
models are pretrained on custom domain corpora and finetuned for specific
tasks, resulting in high costs related to GPU usage and labor. Unfortunately,
recent trends in language modeling have shifted towards enhancing performance
through scaling, further exacerbating the associated costs.
  Introducing GUR: a pretraining framework that combines language modeling and
contrastive learning objectives in a single training step. We select similar
text pairs based on their Longest Common Substring (LCS) from raw unlabeled
documents and train the model using masked language modeling and unsupervised
contrastive learning. The resulting model, GUR, achieves impressive results
without any labeled training data, outperforming all other pretrained baselines
as a retriever at the recall benchmark in a zero-shot setting. Additionally,
GUR maintains its language modeling ability, as demonstrated in our ablation
experiment. Our code is available at \url{https://github.com/laohur/GUR}.