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We challenge this view by identifying that the expensive reranking step can in fact degrade performance. Instead, we propose \\emph{Training-Free Gated Reranking}, which decides whether to rerank the few-shot examples based on the model's uncertainty. Extensive experiments across 8 LLMs, covering 7 NLU datasets and 9 MT domain-language combinations, demonstrate that our approach reduces computational costs by 15\\%-80\\% while improving average performance by up to 2\\%. These findings indicate that higher computational cost does not guarantee better performance, and that reranking is most beneficial when targeted at high-uncertainty instances.","title":"When Reranking Hurts: Uncertainty-Based Gating for Few-Shot Reranking","url":"https://arxiv.org/abs/2606.31087","vendor":"arxiv_cs_ai"},"summary":"arXiv:2606.31087v1 Announce Type: cross \nAbstract: Few-shot selection typically assumes that reranking retrieved examples always improves performance. 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