2025
GASE: Generatively Augmented Sentence Encoding
EMNLP 2025
We propose a training-free approach to improve sentence embeddings leveraging test-time compute by applying generative text models for data augmentation at inference time. Unlike conventional data augmentation that utilises synthetic training data, our approach does not require access to model param