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    <abstract>Although large language models (LLMs) have achieved remarkable performance, the inherent stochasticity of their reasoning processes and varying conclusions present significant challenges. Majority voting or Best-of-N with external verifiers has been explored to mitigate this, but these approaches are limited in applicability or require additional training. To address this problem, we propose a novel framework that Recycles Few-shot examples to verify LLM outputs (ReFeri). Our key idea is to utilize the given few-shot examples not only to generate outputs, but also to evaluate the candidate outputs. Specifically, ReFeri combines a forward confidence score with a backward reconstruction penalty to select candidates that follow few-shot guidance while avoiding demonstration-specific overfitting. Experiments with three different LLMs across seven diverse tasks demonstrate that our framework significantly improves the accuracy of LLMs—achieving an average relative gain of 8.2%—through effective response selection.</abstract>
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%0 Conference Proceedings
%T Training-free LLM Verification via Recycling Few-shot Examples
%A Lee, Dongseok
%A Hong, Jimyung
%A Kim, Dongyoung
%A Kim, Jaehyung
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-459-0
%F lee-etal-2026-training
%X Although large language models (LLMs) have achieved remarkable performance, the inherent stochasticity of their reasoning processes and varying conclusions present significant challenges. Majority voting or Best-of-N with external verifiers has been explored to mitigate this, but these approaches are limited in applicability or require additional training. To address this problem, we propose a novel framework that Recycles Few-shot examples to verify LLM outputs (ReFeri). Our key idea is to utilize the given few-shot examples not only to generate outputs, but also to evaluate the candidate outputs. Specifically, ReFeri combines a forward confidence score with a backward reconstruction penalty to select candidates that follow few-shot guidance while avoiding demonstration-specific overfitting. Experiments with three different LLMs across seven diverse tasks demonstrate that our framework significantly improves the accuracy of LLMs—achieving an average relative gain of 8.2%—through effective response selection.
%U https://aclanthology.org/2026.emnlp-main.1122/
%P 25669-25710

Markdown (Informal)

[Training-free LLM Verification via Recycling Few-shot Examples](https://aclanthology.org/2026.emnlp-main.1122/) (Lee et al., EMNLP 2026)

• Training-free LLM Verification via Recycling Few-shot Examples (Lee et al., EMNLP 2026)

ACL

• Dongseok Lee, Jimyung Hong, Dongyoung Kim, and Jaehyung Kim. 2026. Training-free LLM Verification via Recycling Few-shot Examples. In Proceedings of the 2026 Conference on Empirical Methods in Natural Language Processing, pages 25669–25710, Budapest, Hungary. Association for Computational Linguistics.