​​Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes
Researchers have developed "Distilling step-by-step," a cutting-edge method to train smaller, more efficient task-specific models that outperform large language models (LLMs) while requiring significantly less training data. This innovation promises to revolutionize the practicality of NLP models in real-world applications by reducing both model size and data requirements: a 770M T5 model surpasses a 540B PaLM model using only 80% of available data.
Distilling step-by-step leverages LLM-generated rationales within a multi-task training framework, yielding impressive results across 4 NLP benchmarks. The technique consistently achieves better performance with fewer labeled/unlabeled training examples, surpassing LLMs with substantially smaller model sizes.
Paper link: https://arxiv.org/abs/2305.02301
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dsbs
#deeplearning #nlp #languagemodels #distillation
Researchers have developed "Distilling step-by-step," a cutting-edge method to train smaller, more efficient task-specific models that outperform large language models (LLMs) while requiring significantly less training data. This innovation promises to revolutionize the practicality of NLP models in real-world applications by reducing both model size and data requirements: a 770M T5 model surpasses a 540B PaLM model using only 80% of available data.
Distilling step-by-step leverages LLM-generated rationales within a multi-task training framework, yielding impressive results across 4 NLP benchmarks. The technique consistently achieves better performance with fewer labeled/unlabeled training examples, surpassing LLMs with substantially smaller model sizes.
Paper link: https://arxiv.org/abs/2305.02301
A detailed unofficial overview of the paper: https://andlukyane.com/blog/paper-review-dsbs
#deeplearning #nlp #languagemodels #distillation
