Cluster Validation Indices as Self-Supervised Objectives for Text Representation Learning
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 negatives or graph data? To answer this question, we introduce SilK (Silhouette-guided K-means), which trains on a Cluster Validation Index, an internal measure of cluster quality without using labels. SilK clusters the corpus and then regresses a...
| Authors | Kishor Kumar Bhaumik, Nicolas Roque dos Santos, Neil Shah et al. |
|---|---|
| Primary category | cs.CL |
| Published | 4 October 2026 |
| Last updated at source | 4 October 2026 |
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