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2023.findings-acl.539.pdf
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🔗 Source: aclanthology.org
📊 Size: 238 KB
📄 Pages: 10 pages
⬇️ Downloads: 54

Summary

Researchers propose a neural architecture search (NAS) method for parameter-efficient fine-tuning of large pre-trained language models, using structured and unstructured pruning to learn efficient architectures. Experiments on GLUE tasks demonstrate the effectiveness of the algorithm, which achieves comparable or higher performance than hand-designed methods for the same number of parameters. The study examines the impact of architectural design choices on parameter efficiency and discusses the potential for automated NAS to improve parameter-efficient tuning methods.

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Researchers propose a neural architecture search (NAS) method for parameter-efficient fine-tuning of large pre-trained language models, using structured and...

Technical Information

  • File Format: PDF
  • File Size: 238 KB
  • Pages: 10
  • Language: EN
  • Total Downloads: 54
  • Last Updated: 3 weeks ago

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