A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification

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1. Verfasser: Niimi, Junichiro
Format: Preprint
Veröffentlicht: 2025
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author Niimi, Junichiro
author_facet Niimi, Junichiro
contents With the advance of large language models (LLMs), LLMs have been utilized for the various tasks. However, the issues of variability and reproducibility of results from each trial of LLMs have been largely overlooked in existing literature while actual human annotation uses majority voting to resolve disagreements among annotators. Therefore, this study introduces the straightforward ensemble strategy to a sentiment analysis using LLMs. As the results, we demonstrate that the ensemble of multiple inference using medium-sized LLMs produces more robust and accurate results than using a large model with a single attempt with reducing RMSE by 18.6%.
format Preprint
id arxiv_https___arxiv_org_abs_2504_18884
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification
Niimi, Junichiro
Computation and Language
Artificial Intelligence
With the advance of large language models (LLMs), LLMs have been utilized for the various tasks. However, the issues of variability and reproducibility of results from each trial of LLMs have been largely overlooked in existing literature while actual human annotation uses majority voting to resolve disagreements among annotators. Therefore, this study introduces the straightforward ensemble strategy to a sentiment analysis using LLMs. As the results, we demonstrate that the ensemble of multiple inference using medium-sized LLMs produces more robust and accurate results than using a large model with a single attempt with reducing RMSE by 18.6%.
title A Simple Ensemble Strategy for LLM Inference: Towards More Stable Text Classification
topic Computation and Language
Artificial Intelligence
url https://arxiv.org/abs/2504.18884