Summary of Cosee: Consistency-oriented Signal-based Early Exiting Via Calibrated Sample Weighting Mechanism, by Jianing He et al.
COSEE: Consistency-Oriented Signal-Based Early Exiting via Calibrated Sample Weighting Mechanism
by Jianing He, Qi Zhang, Hongyun Zhang, Xuanjing Huang, Usman Naseem, Duoqian Miao
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 In this paper, researchers propose a novel framework called Consistency-Oriented Signal-based Early Exiting (COSEE) to improve the inference efficiency of pre-trained language models. The COSEE framework leverages a calibrated sample weighting mechanism to enable each classifier to emphasize samples that are more likely to exit at that classifier under various acceleration scenarios. This approach aims to tackle the challenge of flexibly adjusting the speed-up ratio while maintaining consistency between training and testing. The paper demonstrates the effectiveness of COSEE across multiple exiting signals and backbones, yielding a better trade-off between performance and efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Early exiting is a technique that helps improve the efficiency of pre-trained language models by adjusting the number of executed layers for each sample. However, most existing methods don’t account for the difference between training and testing, which can lead to inconsistent results. To solve this problem, researchers propose a new framework called COSEE that uses calibrated sample weights to help classifiers decide which samples are more likely to exit early. This approach allows for flexible adjustments in speed-up ratio while maintaining consistency between training and testing. The paper shows how well COSEE works on the GLUE benchmark with different exiting signals and backbones. |
Keywords
» Artificial intelligence » Inference
