Enhancing Text Classification with a Novel Multi-Agent Collaboration Framework Leveraging BERT

TL;DR

This paper presents a multi-agent collaboration framework that enhances BERT-based text classification accuracy and robustness by integrating specialized agents for analysis and consensus, achieving significant performance improvements.

Contribution

Introduces a novel multi-agent system that collaborates with BERT to improve text classification accuracy and robustness across diverse tasks.

Findings

Achieves a 5.5% increase in accuracy over standard BERT classifiers.

Demonstrates effectiveness across multiple benchmark datasets.

Advances multi-agent collaboration in natural language processing.

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.