Small Language Models: Dispersion Loss Offsets Embedding Condensation

A study finds that dispersion loss counteracts the natural condensation of embeddings in small language models, impacting their performance and representation quality. This insight is important for optimizing training strategies and understanding trade-offs in compact neural networks.

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