GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation
<|reference_start|>GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced Distillation: Pre-trained language models have become an integral component of question-answering systems, achieving remarkable performance. For practical deployment, it is critical to carry out knowledge distillation to preserve high performance under computational constraints. In this paper, we address a key question: given the importance of unsupervised distillation for student performance, how does one effectively ensemble knowledge from multiple teachers at this stage without the guidance of ground-truth labels? We propose a novel algorithm, GOVERN, to tackle this issue. GOVERN has demonstrated significant improvements in both offline and online experiments. The proposed algorithm has been successfully deployed in a real-world commercial question-answering system.<|reference_end|>
@article{zhou2024govern:,
    title={GOVERN: Gradient Orientation Vote Ensemble for Multi-Teacher Reinforced
  Distillation},
    author={Wenjie Zhou, Zhenxin Ding, Xiaodong Zhang, Haibo Shi, Junfeng Wang,
  Dawei Yin},
    journal={arXiv preprint arXiv:2405.03764},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03764},
    primaryClass={cs.CL cs.IR}
}