  Competitive debate is a comprehensive and complex computational argumentation
task. Large Language Models (LLMs) encounter hallucinations and lack
competitiveness in this task. To address these challenges, we introduce Agent
for Debate (Agent4Debate), a dynamic, multi-agent framework based on LLMs
designed to enhance their capabilities in competitive debate. Drawing
inspiration from human behavior in debate preparation and execution,
Agent4Debate employs a collaborative architecture where four specialized agents
(Searcher, Analyzer, Writer, and Reviewer) dynamically interact and cooperate.
These agents work throughout the debate process, covering multiple stages from
initial research and argument formulation to rebuttal and summary. To
comprehensively evaluate framework performance, we construct the Chinese Debate
Arena, comprising 66 carefully selected Chinese debate motions. We recruite ten
experienced human debaters and collect records of 200 debates involving
Agent4Debate, baseline models, and humans. The evaluation employs the Debatrix
automatic scoring system and professional human reviewers based on the
established Debatrix-Elo and Human-Elo ranking. Experimental results indicate
that the state-of-the-art Agent4Debate exhibits capabilities comparable to
those of humans. Furthermore, ablation studies demonstrate the effectiveness of
each component in the agent structure.
