Dispersion Loss Counteracts Embedding Condensation in Small Language Models

Researchers are studying small language models. They have identified embedding condensation issues. This phenomenon affects model performance. Dispersion loss is proposed as a solution. It helps counteract the condensation effect. The research is detailed in a new project. This approach aims to improve model efficiency. It provides a new way to manage embeddings.