Abstract
We introduce a novel multi-agent collaboration framework designed to enhance the accuracy and robustness of text classification models. Leveraging BERT as the primary classifier, our framework dynamically escalates low-confidence predictions to a specialized multi-agent system comprising Lexical, Contextual, Logic, Consensus, and Explainability agents. This collaborative approach allows for comprehensive analysis and consensus-driven decision-making, significantly improving classification performance across diverse text classification tasks. Empirical evaluations on benchmark datasets demonstrate that our framework achieves a 5.5% increase in accuracy compared to standard BERT-based classifiers, underscoring its effectiveness and academic novelty in advancing multi-agent systems within natural language processing.
Abstract (translated)
我们介绍了一种新颖的多代理协作框架，旨在提高文本分类模型的准确性和鲁棒性。该框架利用BERT作为主要分类器，并且能够动态地将低置信度预测提升到一个由词汇（Lexical）、上下文(Contextual)、逻辑(Logic)、共识(Consensus)和解释力(Explainability)代理组成的专门化多代理系统中进行处理。 这种协作方法允许进行全面分析和基于共识的决策制定，从而在各种文本分类任务上显著提高分类性能。我们在基准数据集上的实证评估表明，与标准BERT基线分类器相比，我们的框架实现了5.5%的准确率提升，这充分证明了其有效性和在自然语言处理领域推进多代理系统的学术新颖性。
URL
https://arxiv.org/abs/2502.18653
