Improving Spam Detection with a Multi-Agent Debate Framework
Ronghong Huang · 2024
Spam detection is a critical area within information security, where machine learning (ML) and deep learning (DL) have significantly advanced the state-of-the-art. This paper presents an advanced multi-agent debate framework designed to enhance the accuracy and practicality of spam detection within the realm of information security. By integrating multiple Large Language Models (LLMs), the framework employs innovative discussion strategies and tailored algorithms to simulate complex human evaluation processes. The multi-agent system assigns distinct roles to individual agents, each contributing uniquely to the discussion dynamics, which facilitates a multifaceted evaluation akin to human judgment. The framework’s collaborative approach not only bolsters the authenticity of responses but also strengthens the system’s capacity to handle complex tasks. Through rigorous experimentation, we demonstrate that our multi-agent debate framework significantly outperforms traditional single-agent methods in terms of precision and robustness. The framework’s ability to adapt to the evolving tactics of spammers and its potential to be optimized for various datasets and tasks make it a promising tool for enhancing automated text evaluations.