Understanding and Improving Noisy Embedding Techniques in Instruction Finetuning
Researchers have introduced SymNoise, a novel method for fine-tuning language models that utilizes symmetric noise in embeddings. This technique aims to improve model performance by more precisely regulating local curvature, outperforming the existing state-of-the-art method, NEFTune. In experiments, SymNoise significantly boosted the AlpacaEval score of LLaMA-2-7B fine-tuned with Alpaca from 29.79% to 69.04%, a 6.7% improvement over NEFTune's 64.69%. The method also demonstrated consistent superiority over NEFTune across various models and datasets. AI
IMPACT This new fine-tuning technique offers a significant performance boost for language models, potentially improving their capabilities across various applications.