Summary of Confidence Preservation Property in Knowledge Distillation Abstractions, by Dmitry Vengertsev et al.
Confidence Preservation Property in Knowledge Distillation Abstractions
by Dmitry Vengertsev, Elena Sherman
First submitted to arxiv on: 21 Jan 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a solution to improve the efficiency of deep neural network language models used in social media platforms for sentiment analysis and content understanding. Currently, large-scale models like BERT are complex and expensive to operate, which limits their adoption. To address this issue, the authors propose a knowledge distillation compression technique that trains a distilled model to mimic the classification behavior of the original model. The goal is to develop a more efficient model that preserves the key properties of the original model. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Social media platforms use deep learning models like BERT to detect harmful content on posts and comments. While these models are accurate, they require significant computational resources, making them expensive to operate. To solve this problem, experts compress complex models using knowledge distillation, which trains a distilled model to behave similarly to the original one. This paper investigates whether compressed TinyBERT models maintain the confidence levels of the original BERT models and explores how preserving confidence could help optimize the compression process. |
Keywords
* Artificial intelligence * Bert * Classification * Deep learning * Knowledge distillation * Neural network
