New AI Training Method Boosts Performance By 20% With Less Data
New AI training method cuts data needs by 50% & boosts performance by 20%. Selective Self-to-Supervised Fine-Tuning (S2SFT) combines self-supervised & supervised learning for better generalization.
This is a Plain English Papers summary of a research paper called New AI Training Method Cuts Data Needs in Half While Boosting Performance by 20%. If you like these kinds of analysis, you should join AImodels.fyi or follow us on Twitter. Overview Introduces a new approach for fine-tuning large language models called Selective Self-to-Supervised Fine-Tuning (S2SFT) Combines self-supervised and supervised learning to improve model generalization Achieves better performance while using less training data Reduces catastrophic forgetting during fine-tuning Shows significant improvements...