用遗传算法让AI动态生成辩论策略，提升对抗性与真实性。
LLMs as Debate Partners: Utilizing Genetic Algorithms and Adversarial Search for Adaptive Arguments
- 结合遗传算法与对抗搜索，让AI实时优化辩论策略。
- 23场辩论中AI平均得分2.72，人类2.67，事实准确率达92%。
- 适合想提升辩论能力的用户，尤其关注真实性和挑战性。
本文提出DebateBrawl，一个融合大语言模型（LLMs）、遗传算法（GA）和对抗搜索（AS）的AI辩论平台，旨在克服传统LLMs在战略规划上的不足。系统通过进化优化与博弈论技术，实现自适应、高保真的辩论交互。23场真人-AI对辩实验显示，AI平均得分为2.72（满分10），人类为2.67，双方表现接近。85%用户表示辩论技巧显著提升，78%认为对手挑战适中。系统事实准确率达92%，远超人类仅78%。其多样化论证能力有效缓解了AI辅助对话中的可信度问题。论文还探讨了AI在说服场景中的伦理风险，并通过强事实核查与决策透明机制保障负责任部署。
原文摘要 · Abstract (English)
This paper introduces DebateBrawl, an innovative AI-powered debate platform that integrates Large Language Models (LLMs), Genetic Algorithms (GA), and Adversarial Search (AS) to create an adaptive and engaging debating experience. DebateBrawl addresses the limitations of traditional LLMs in strategic planning by incorporating evolutionary optimization and game-theoretic techniques. The system demonstrates remarkable performance in generating coherent, contextually relevant arguments while adapting its strategy in real-time. Experimental results involving 23 debates show balanced outcomes between AI and human participants, with the AI system achieving an average score of 2.72 compared to the human average of 2.67 out of 10. User feedback indicates significant improvements in debating skills and a highly satisfactory learning experience, with 85% of users reporting improved debating abilities and 78% finding the AI opponent appropriately challenging. The system's ability to maintain high factual accuracy (92% compared to 78% in human-only debates) while generating diverse arguments addresses critical concerns in AI-assisted discourse. DebateBrawl not only serves as an effective educational tool but also contributes to the broader goal of improving public discourse through AI-assisted argumentation. The paper discusses the ethical implications of AI in persuasive contexts and outlines the measures implemented to ensure responsible development and deployment of the system, including robust fact-checking mechanisms and transparency in decision-making processes.
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