Enhancing Text Classification with a Novel Multi-Agent Collaboration Framework Leveraging BERT
This work addresses text classification challenges in natural language processing, but it is incremental as it builds on existing BERT methods with a multi-agent extension.
The paper tackled the problem of improving text classification accuracy and robustness by introducing a multi-agent collaboration framework that uses BERT as a base classifier and escalates low-confidence predictions to specialized agents for analysis and consensus. The result was a 5.5% increase in accuracy compared to standard BERT-based classifiers on benchmark datasets.
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.