Growing Smaller Language Models Using Knowledge Distillation from Larger Models

The rapid development of natural language processing technologies has necessitated models that are both high-performing and computationally efficient, posing a challenge for resource-constrained environments. Knowledge distillation, a technique where a smaller model learns from a larger pre-trained model, offers a novel and significant solution by enhancing the capabilities of the smaller model while maintaining a reduced computational footprint. This research explores the application of knowledge distillation to finetune GPT-Neo using Mistral Large, resulting in notable improvements in accuracy, precision, recall, and F1-score across tasks such as text generation, translation, summarization, and question-answering. Comprehensive evaluations demonstrated substantial reductions in inference time, memory usage, and energy consumption, highlighting the practical benefits of the approach. The finetuned GPT-Neo exhibited enhanced linguistic proficiency, coherence, fluency, and contextual accuracy, underscoring the effectiveness of knowledge distillation in optimizing model performance. The findings validate knowledge distillation as a robust method for advancing natural language processing technologies, ensuring high performance in environments with limited computational resources.