多数决策：LLM集成是内容分类的获胜方法
Majority Rules: LLM Ensemble is a Winning Approach for Content Categorization
AI总结：
本研究提出eLLM集成方法，通过整合多个模型提升无结构文本分类的准确性和鲁棒性，实现接近人类专家水平的性能。
摘要：
本研究介绍了一种使用大型语言模型（LLMs）进行无结构文本分类的集成框架。通过整合多个模型，集成大型语言模型（eLLM）框架解决了单个系统常见的弱点，包括不一致性、幻觉、类别膨胀和误分类。eLLM方法在F1分数上比最强的单个模型提高了高达65%。我们通过集体决策的数学模型正式化集成过程，并建立了原则性的聚合标准。使用交互式广告局（IAB）分层分类法，在相同零样本条件下，我们在8660个人工标注的语料库上评估了十种最先进的LLMs。结果表明，由于将语义丰富的文本压缩成稀疏类别表示，单个模型性能趋于平缓，而eLLM在鲁棒性和准确性方面均有提升。通过多样化的模型 consortium，eLLM达到接近人类专家水平的性能，提供了一种可扩展且可靠的基于分类法的分类解决方案，可能显著减少对人类专家标注的依赖。
英文摘要：
This study introduces an ensemble framework for unstructured text categorization using large language models (LLMs). By integrating multiple models, the ensemble large language model (eLLM) framework addresses common weaknesses of individual systems, including inconsistency, hallucination, category inflation, and misclassification. The eLLM approach yields a substantial performance improvement of up to 65\% in F1-score over the strongest single model. We formalize the ensemble process through a mathematical model of collective decision-making and establish principled aggregation criteria. Using the Interactive Advertising Bureau (IAB) hierarchical taxonomy, we evaluate ten state-of-the-art LLMs under identical zero-shot conditions on a human-annotated corpus of 8{,}660 samples. Results show that individual models plateau in performance due to the compression of semantically rich text into sparse categorical representations, while eLLM improves both robustness and accuracy. With a diverse consortium of models, eLLM achieves near human-expert-level performance, offering a scalable and reliable solution for taxonomy-based classification that may significantly reduce dependence on human expert labeling.
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