Papers with prompt retrieval module

1 papers
Automated Few-Shot Classification with Instruction-Finetuned Language Models (2023.findings-emnlp)

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Challenge: Existing few-shot learning approaches combine language models with prompts, but they often require domain knowledge and substantial guesswork.
Approach: They propose a method to eliminate the need for handcrafted prompts by generating two distinct, semantically meaningful class descriptions and a selection mechanism via cross-validation.
Outcome: The proposed method outperforms state-of-the-art few-shot learning methods over 12 datasets, spanning 8 classification tasks.

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