Summary of Structural Pruning Of Pre-trained Language Models Via Neural Architecture Search, by Aaron Klein et al.
Structural Pruning of Pre-trained Language Models via Neural Architecture Search
by Aaron Klein, Jacek Golebiowski, Xingchen Ma, Valerio Perrone, Cedric Archambeau
First submitted to arxiv on: 3 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 A novel approach to neural architecture search (NAS) is proposed to optimize fine-tuned pre-trained language models (PLMs) like BERT or RoBERTa for efficient inference in real-world applications. By structurally pruning the model, the authors aim to find a balance between efficiency, measured by model size and latency, and generalization performance. The proposed NAS method utilizes two-stage weight-sharing approaches to accelerate the search process. Unlike traditional pruning methods, this multi-objective approach identifies a Pareto optimal set of sub-networks for flexible compression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores new ways to make language models like BERT or RoBERTa work better on devices with limited power and memory. The authors want to find the best way to reduce the size of these models without losing their ability to understand natural language. They use a special search method to identify the most important parts of the model that can be safely removed, making it faster and more efficient. |
Keywords
» Artificial intelligence » Bert » Generalization » Inference » Pruning
