Majority Rules: LLM Ensemble is a Winning Approach for Content Categorization

• URL: http://arxiv.org/abs/2511.15714v1
• Date: Tue, 11 Nov 2025 05:10:09 GMT
• Title: Majority Rules: LLM Ensemble is a Winning Approach for Content Categorization
• Authors: Ariel Kamen, Yakov Kamen,
• Abstract summary: This study introduces an ensemble framework for unstructured text categorization using large language models (LLMs)<n>By integrating multiple models, the ensemble large language model (eLLM) framework addresses common weaknesses of individual systems.<n>eLLM achieves near human-expert-level performance, offering a scalable and reliable solution for taxonomy-based classification.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

Related papers

• Beyond Accuracy: Characterizing Code Comprehension Capabilities in (Large) Language Models [4.841487377596519]
  This paper investigates whether Large Language Models' code-comprehension performance aligns with traditional human-centric software metrics.<n>We introduce a diagnostic framework that reframes code understanding as a binary input-output consistency task.
  arXiv  Detail & Related papers  (2026-01-19T10:58:24Z)
• The Law of Multi-Model Collaboration: Scaling Limits of Model Ensembling for Large Language Models [54.51795784459866]
  We propose a theoretical framework of performance scaling for multi-model collaboration.<n>We show that multi-model systems follow a power-law scaling with respect to the total parameter count.<n> ensembles of heterogeneous model families achieve better performance scaling than those formed within a single model family.
  arXiv  Detail & Related papers  (2025-12-29T09:55:12Z)
• LLM-MemCluster: Empowering Large Language Models with Dynamic Memory for Text Clustering [52.41664454251679]
  Large Language Models (LLMs) are reshaping unsupervised learning by offering an unprecedented ability to perform text clustering.<n>Existing methods often rely on complex pipelines with external modules, sacrificing a truly end-to-end approach.<n>We introduce LLM-MemCluster, a novel framework that reconceptualizes clustering as a fully LLM-native task.
  arXiv  Detail & Related papers  (2025-11-19T13:22:08Z)
• Order from Chaos: Comparative Study of Ten Leading LLMs on Unstructured Data Categorization [0.0]
  This study presents a comparative evaluation of ten state-of-the-art large language models (LLMs) applied to unstructured text categorization.<n>The analysis employed a uniform dataset of 8,660 human-annotated samples and identical zero-shot prompts to ensure methodological consistency across all models.<n>Results show that contemporary LLMs achieve only moderate classic performance, with average scores of 34% accuracy, 42% precision, 45% recall, and 41% F1-score.
  arXiv  Detail & Related papers  (2025-10-14T02:15:01Z)
• Prismatic Synthesis: Gradient-based Data Diversification Boosts Generalization in LLM Reasoning [77.120955854093]
  We show that data diversity can be a strong predictor of generalization in language models.<n>We introduce G-Vendi, a metric that quantifies diversity via the entropy of model-induced gradients.<n>We present Prismatic Synthesis, a framework for generating diverse synthetic data.
  arXiv  Detail & Related papers  (2025-05-26T16:05:10Z)
• Model Utility Law: Evaluating LLMs beyond Performance through Mechanism Interpretable Metric [99.56567010306807]
  Large Language Models (LLMs) have become indispensable across academia, industry, and daily applications.<n>One core challenge of evaluation in the large language model (LLM) era is the generalization issue.<n>We propose Model Utilization Index (MUI), a mechanism interpretability enhanced metric that complements traditional performance scores.
  arXiv  Detail & Related papers  (2025-04-10T04:09:47Z)
• SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment [9.952187981270326]
  We propose the Sentiment Cross-Lingual Recognition and Logic Framework (SentiXRL)<n>SentiXRL incorporates two modules,an emotion retrieval enhancement module to improve sentiment classification accuracy in complex contexts through historical dialogue and logical reasoning.<n>We have validated SentiXRL's superiority on multiple standard datasets, outperforming existing models on CPED and CH-SIMS,and achieving overall better performance on MELD,Emorynlp and IEMOCAP.
  arXiv  Detail & Related papers  (2024-11-27T09:18:26Z)
• Matchmaker: Self-Improving Large Language Model Programs for Schema Matching [60.23571456538149]
  We propose a compositional language model program for schema matching, comprised of candidate generation, refinement and confidence scoring. Matchmaker self-improves in a zero-shot manner without the need for labeled demonstrations. Empirically, we demonstrate on real-world medical schema matching benchmarks that Matchmaker outperforms previous ML-based approaches.
  arXiv  Detail & Related papers  (2024-10-31T16:34:03Z)
• Model-GLUE: Democratized LLM Scaling for A Large Model Zoo in the Wild [84.57103623507082]
  This paper introduces Model-GLUE, a holistic Large Language Models scaling guideline.<n>We benchmark existing scaling techniques, especially selective merging, and variants of mixture.<n>We then formulate an optimal strategy for the selection and aggregation of a heterogeneous model zoo.<n>Our methodology involves the clustering of mergeable models and optimal merging strategy selection, and the integration of clusters.
  arXiv  Detail & Related papers  (2024-10-07T15:55:55Z)
• Determine-Then-Ensemble: Necessity of Top-k Union for Large Language Model Ensembling [23.447466392929712]
  Large language models (LLMs) exhibit varying strengths and weaknesses across different tasks.<n>Existing LLM ensembling methods often overlook model compatibility and struggle with inefficient alignment of probabilities.<n>We introduce the textscUnion textscTop-$k$ textscEnsembling (textscUniTE), a novel approach that efficiently combines models by focusing on the union of the top-k tokens from each model.
  arXiv  Detail & Related papers  (2024-10-03T08:42:38Z)
• Data Efficient Evaluation of Large Language Models and Text-to-Image Models via Adaptive Sampling [3.7467864495337624]
  SubLIME is a data-efficient evaluation framework for text-to-image models. Our approach ensures statistically aligned model rankings compared to full datasets. We leverage the HEIM leaderboard to cover 25 text-to-image models on 17 different benchmarks.
  arXiv  Detail & Related papers  (2024-06-21T07:38:55Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.