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

• URL: http://arxiv.org/abs/2502.18653v1
• Date: Tue, 25 Feb 2025 21:30:16 GMT
• Title: Enhancing Text Classification with a Novel Multi-Agent Collaboration Framework Leveraging BERT
• Authors: Hediyeh Baban, Sai A Pidapar, Aashutosh Nema, Sichen Lu,
• Abstract summary: We introduce a novel multi-agent collaboration framework designed to enhance the accuracy and robustness of text classification models.<n>Our framework dynamically escalates low-confidence predictions to a specialized multi-agent system.<n> Empirical evaluations on benchmark datasets demonstrate that our framework achieves a 5.5% increase in accuracy.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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