Cluster Validation Indices as Self-Supervised Objectives for Text Representation Learning

Excerpt

arXiv:2610.04830v1 Announce Type: cross Abstract: Self-supervised fine-tuning refines the embedding space of a pretrained language encoder without labels. However, the commonly used approaches are computationally expensive. Specifically, contrastive learning-based methods need multiview data and in-batch negative examples, while negative-free approaches require auxiliary graphs/networks. An interesting question arises: can self-supervised fine-tuning be done without relying on either additional