Before revision:  In this paper, we show how large pretrained language models can be leveraged to obtain high-quality embeddings without requiring any labeled data, finetuning or modifications to their pretraining objective: We utilize their generative abilities to generate entire datasets of labeled text pairs from scratch, which can then be used for regular finetuning of much smaller models.
After revision:  In this paper, we show how large PLMs can be leveraged to obtain high-quality embeddings without requiring any labeled data, finetuning or modifications to their pretraining objective: We utilize their generative abilities to generate entire datasets of labeled text pairs from scratch, which can then be used for regular finetuning of much smaller models.