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2022.naacl-main.441.pdf
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🔗 Source: aclanthology.org
📊 Size: 231 KB
📄 Pages: 7 pages
⬇️ Downloads: 318

Summary

Researchers propose a simple method for predicting the performance of abductive natural language inference (αNLI) models without fine-tuning, by comparing sentence embeddings with cosine similarity. They find a strong correlation (Pearson coefficient of 0.65) between the accuracy of this approach and the accuracy of fine-tuning a classifier on top of these embeddings, allowing for significant time savings in model selection.

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Description

Researchers propose a simple method for predicting the performance of abductive natural language inference (αNLI) models without fine-tuning, by comparing...

Technical Information

  • File Format: PDF
  • File Size: 231 KB
  • Pages: 7
  • Language: EN
  • Total Downloads: 318
  • Last Updated: 9 hours ago

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