用树状结构让大模型更会辩论，策略性地选重点、控节奏。
Strategic Planning and Rationalizing on Trees Make LLMs Better Debaters
- 构建演练树与辩论流树，提前预判攻防并追踪辩论状态。
- 在关键节点分配时间，使论点说服力提升15.6%，赢得率提高10%。
- 适合研究对话策略、智能辩论系统的人参考。
赢得竞争性辩论需要复杂的推理与论证能力。竞争辩论面临两大挑战：(1) 时间限制迫使辩手必须战略性选择论述重点，而非覆盖所有论点；(2) 辩论的说服力依赖于论点之间的动态互动，单一最终结论无法评估。为此，我们提出 TreeDebater，一种新型辩论框架。引入两种树结构：演练树用于预判攻击与防御以评估论点强度，辩论流树用于追踪辩论进展以识别当前有效行动。TreeDebater 根据时间预算分配候选动作，并通过发言时间控制器与模拟观众反馈不断修正陈述。人类评估显示，其在阶段级说服力上较 DeepSeek 提升15.6%，在辩论级观点转变胜率上提升10%。深入分析表明，TreeDebater 能更好聚焦关键辩论行为，其策略与人类辩论专家高度一致。
原文摘要 · Abstract (English)
Winning competitive debates requires sophisticated reasoning and argument skills. There are unique challenges in the competitive debate: (1) The time constraints force debaters to make strategic choices about which points to pursue rather than covering all possible arguments; (2) The persuasiveness of the debate relies on the back-and-forth interaction between arguments, which a single final game status cannot evaluate. To address these challenges, we propose TreeDebater, a novel debate framework that excels in competitive debate. We introduce two tree structures: the Rehearsal Tree and Debate Flow Tree. The Rehearsal Tree anticipates the attack and defenses to evaluate the strength of the claim, while the Debate Flow Tree tracks the debate status to identify the active actions. TreeDebater allocates its time budget among candidate actions and uses the speech time controller and feedback from the simulated audience to revise its statement. The human evaluation on both the stage-level and the debate-level comparison shows that our TreeDebater outperforms the state-of-the-art multi-agent debate system, with a +15.6% improvement in stage-level persuasiveness with DeepSeek and +10% debate-level opinion shift win. Further investigation shows that TreeDebater shows better strategies in limiting time to important debate actions, aligning with the strategies of human debate experts.
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