Domain Knowledge Distillation

Domain knowledge distillation aims to transfer the expertise of large, computationally expensive language models (LLMs) to smaller, more efficient models. Current research focuses on improving the efficiency and accuracy of this transfer, particularly addressing challenges like domain mismatch between the teacher and student models and the lack of access to the teacher's training data. This involves developing novel distillation algorithms and frameworks that dynamically adapt to domain-specific knowledge gaps and leverage techniques like mixup learning and prompt engineering. The ultimate goal is to create smaller, faster, and more deployable models while retaining the performance of their larger counterparts, impacting various fields from autonomous driving to multilingual text processing.