The paper introduces Universal Language Model Fine-tuning (ULMFiT), a novel transfer learning method for Natural Language Processing (NLP) tasks. ULMFiT leverages pre-trained language models to fine-tune task-specific classifiers, significantly outperforming existing methods on six text classification datasets. The authors propose several key techniques, including discriminative fine-tuning, slanted triangular learning rates, and gradual unfreezing, which prevent catastrophic forgetting and improve convergence. ULMFiT demonstrates superior performance with only 100 labeled examples, matching the performance of training from scratch with 100× more data. The method is open-sourced, and the authors provide extensive ablation studies to validate its effectiveness.The paper introduces Universal Language Model Fine-tuning (ULMFiT), a novel transfer learning method for Natural Language Processing (NLP) tasks. ULMFiT leverages pre-trained language models to fine-tune task-specific classifiers, significantly outperforming existing methods on six text classification datasets. The authors propose several key techniques, including discriminative fine-tuning, slanted triangular learning rates, and gradual unfreezing, which prevent catastrophic forgetting and improve convergence. ULMFiT demonstrates superior performance with only 100 labeled examples, matching the performance of training from scratch with 100× more data. The method is open-sourced, and the authors provide extensive ablation studies to validate its effectiveness.