FINE-TUNING STATE-OF-THE-ART MODELS

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Challenge:	Generatively Augmented Sentence Encoding variates the input text by paraphrasing, summarizing, or extracting keywords, followed by pooling the original and synthetic embeddings.
Approach:	They propose a training-free approach to improve sentence embeddings by applying generative text models for data augmentation at inference time.
Outcome:	The proposed approach does not require access to model parameters or computational resources typically required for fine-tuning state-of-the-art models.

Papers with fine-tuning state-of-the-art models