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This research introduces ReFeri, a training-free framework that leverages existing few-shot examples to simultaneously generate and verify LLM outputs without requiring external verifiers or additional model training. By combining forward confidence scoring with backward reconstruction penalties, the method effectively mitigates stochasticity in reasoning processes while avoiding overfitting to specific demonstrations. The approach demonstrates significant accuracy improvements across diverse tasks, offering a practical pathway for enhancing reliability in current large language model deployments.
Training-free LLM Verification via Recycling Few-shot Examples
arXiv:2506.17251v3 Announce Type: replace-cross 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: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.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2506.17251 [cs.LG]
(or arXiv:2506.17251v3 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2506.17251 Focus to learn more arXiv-issued DOI via DataCite
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