用多智能体协作提升BERT文本分类准确率
Enhancing Text Classification with a Novel Multi-Agent Collaboration Framework Leveraging BERT
- 低置信度预测自动触发五个专业智能体协同分析
- 在基准数据集上比标准BERT提升5.5%准确率
- 适合需要高精度与可解释性的实际文本分类场景
我们提出一种新型多智能体协作框架，用于提升文本分类模型的准确率与鲁棒性。以BERT作为主分类器，当预测置信度较低时，系统会动态调用包含词汇、上下文、逻辑、共识和可解释性五类智能体组成的专用协作系统。该协同机制实现全面分析与共识决策，在多种文本分类任务中显著提升性能。在基准数据集上的实证评估显示，该框架相比标准BERT分类器准确率提升5.5%，验证了其在自然语言处理中多智能体系统应用的有效性与学术创新性。
原文摘要 · Abstract (English)
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.
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