Papers with LM-CPPF

1 papers
LM-CPPF: Paraphrasing-Guided Data Augmentation for Contrastive Prompt-Based Few-Shot Fine-Tuning (2023.acl-short)

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Challenge: Recent advances in pre-trained language models have been limited when fine-tuned on small datasets.
Approach: They propose to add contrastive learning to prompt-based fine-tuning to improve model performance.
Outcome: The proposed approach outperforms other methods on multiple text classification benchmarks.

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