# Rethinking the Knowledge Distillation From the Perspective of Model Calibration

arXiv: https://arxiv.org/abs/2111.01684 · HTML: https://mlsanity.com/paper/2111.01684/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Knowledge distillation for LLMs, one of 12 directions in Knowledge distillation.
- Builds on 4 of its 4 references that are in the map.
- First posted on arXiv 31 Oct 2021.

## Placed in

- Knowledge distillation / [Knowledge distillation for LLMs](https://mlsanity.com/distill/knowledge-distillation-for-llms/)

## Builds on (most cited first, 4 of 4)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Improved Knowledge Distillation via Teacher Assistant](https://mlsanity.com/paper/1902.03393/) (2019)
- [On the Efficacy of Knowledge Distillation](https://mlsanity.com/paper/1910.01348/) (2019)

## Built on by (most cited first, 2 of 2)

- [The Role of Teacher Calibration in Knowledge Distillation](https://mlsanity.com/paper/2508.20224/) (2025)
- [SHAPE: Coalition-Aware Expert Pruning for Sparse Mixture-of-Experts LLMs](https://mlsanity.com/paper/2606.09886/) (2026)

## Abstract (arXiv)

Recent years have witnessed dramatically improvements in the knowledge distillation, which can generate a compact student model for better efficiency while retaining the model effectiveness of the teacher model. Previous studies find that: more accurate teachers do not necessary make for better teachers due to the mismatch of abilities. In this paper, we aim to analysis the phenomenon from the perspective of model calibration. We found that the larger teacher model may be too over-confident, thus the student model cannot effectively imitate. While, after the simple model calibration of the teacher model, the size of the teacher model has a positive correlation with the performance of the student model.