Uncertainty-based gating for few-shot reranking reduces compute by 15-80% and improves performance by up to 2% across 8 LLMs, 7 NLU datasets, and 9 MT settings.
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ACSESS automatically combines 23 sample selection strategies to outperform individual strategies in few-shot learning on text and image datasets.
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When Reranking Hurts: Uncertainty-Based Gating for Few-Shot Reranking
Uncertainty-based gating for few-shot reranking reduces compute by 15-80% and improves performance by up to 2% across 8 LLMs, 7 NLU datasets, and 9 MT settings.
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Automatic Combination of Sample Selection Strategies for Few-Shot Learning
ACSESS automatically combines 23 sample selection strategies to outperform individual strategies in few-shot learning on text and image datasets.