When Reranking Hurts: Uncertainty-Based Gating for Few-Shot Reranking

Few-shot selection typically assumes that reranking retrieved examples always improves performance. We challenge this view by identifying that the expensive reranking step can in fact degrade performance. Instead, we propose 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 18 MT domain-direction combinations, demonstrate that our approach reduces average computational costs by approximately 20% for MT and 54% for NLU at a calibrated operating point, while matching the performance of full reranking. These findings indicate that higher computational cost does not guarantee better performance, and that reranking is most beneficial when targeted at high-uncertainty instances.