Papers with MEAL

1 papers
MEAL: Stable and Active Learning for Few-Shot Prompting (2023.findings-emnlp)

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Challenge: Existing methods for few-shot classification have high variance across different sets of few shots and finetuning runs.
Approach: They propose novel ensembling methods that significantly reduce run variability and introduce a new active learning criterion for *data selection*.
Outcome: The proposed method significantly reduces run variability and improves performance on five tasks.

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