This study explores the potential of small language model(SLM) ensembles to achieve accuracy comparable to proprietary large language models (LLMs). We propose Ensemble Bayesian Inference (EBI), a novel approach that applies Bayesian estimation to combine judgments from multiple SLMs, allowing them to exceed the performance limitations of individual models. Our experiments on diverse tasks(aptitude assessments and consumer profile analysis in both Japanese and English) demonstrate EBI's effectiveness. Notably, we analyze cases where incorporating models with negative Lift values into ensembles improves overall performance, and we examine the method's efficacy across different languages. These findings suggest new possibilities for constructing high-performance AI systems with limited computational resources and for effectively utilizing models with individually lower performance. Building on existing research on LLM performance evaluation, ensemble methods, and open-source LLM utilization, we discuss the novelty and significance of our approach.

本研究解决了小型语言模型(SLM)在精确性方面无法与大型语言模型(LLM)竞争的问题，提出了一种新的集成贝叶斯推断(EBI)方法，通过结合多个SLM的判断，超越了单个模型的性能限制。实验结果表明，EBI在多种任务中均表现出色，尤其在包含负Lift值的模型时，对整体性能的提升显示了该方法在构建高性能AI系统及其资源有限情境下的潜力。

集成贝叶斯推断：利用小型语言模型实现档案匹配任务中的大语言模型级准确性