Papers with teacher and student
Structural Knowledge Distillation: Tractably Distilling Information for Structured Predictor (2021.acl-long)
Copied to clipboard
Xinyu Wang, Yong Jiang, Zhaohui Yan, Zixia Jia, Nguyen Bach, Tao Wang, Zhongqiang Huang, Fei Huang, Kewei Tu
| Challenge: | Knowledge distillation is a technique to transfer knowledge between models, typically from a large model (the teacher) to a more fine-grained one (the student). |
| Approach: | They propose a factorized form of the knowledge distillation objective for structured prediction which is tractable for many typical choices of the teacher and student models. |
| Outcome: | The proposed model is able to transfer knowledge between teacher and student models without loss of accuracy under four different scenarios. |
Differentially Private Knowledge Distillation via Synthetic Text Generation (2024.findings-acl)
Copied to clipboard
| Challenge: | Large Language models (LLMs) are achieving state-of-the-art performance in many downstream tasks, but data privacy is a major challenge for practitioners. |
| Approach: | They propose a differentially private knowledge distillation algorithm that exploits the knowledge of a teacher LLM and a student's output distribution. |
| Outcome: | The proposed algorithm significantly improves the utility over baselines on the Big Patent dataset, with strong privacy parameters, =2. |