Sequence Level Knowledge Distillation

Sequence-level knowledge distillation aims to transfer the capabilities of large, computationally expensive language models (LLMs) to smaller, more efficient student models by focusing on the entire output sequence rather than individual tokens. Current research emphasizes addressing challenges like long-tailed data distributions and improving the abstractiveness and diversity of generated sequences, often employing techniques such as multi-stage training, f-divergence minimization, and n-best reranking to enhance student model performance. This approach holds significant promise for deploying LLMs in resource-constrained environments and improving the efficiency of various natural language processing tasks, including machine translation and summarization.