Strategic Planning and Rationalizing on Trees Make LLMs Better Debaters (2505.14886v1)

Abstract: 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. Further investigation shows that TreeDebater shows better strategies in limiting time to important debate actions, aligning with the strategies of human debate experts.

Strategic Planning and Rationalizing in Competitive Debate with LLMs

The paper "Strategic Planning and Rationalizing on Trees Make LLMs Better Debaters" introduces TreeDebater, a novel framework designed to enhance the capabilities of LLMs in competitive debating. This advancement addresses the inherent challenges faced by AI in debate settings, such as strategic time allocation and the dynamic interplay of arguments. TreeDebater distinguishes itself by employing two central tree structures: the Rehearsal Tree and the Debate Flow Tree, each serving crucial roles in optimizing debate strategies.

Key Innovations and Methodology

Rehearsal Tree: This structure helps the AI anticipate and prepare for potential counterarguments it might face during a debate. Constructed through a top-down approach, the tree organizes claims and arguments hierarchically up to a specified depth, allowing the AI to evaluate argument strength via predefined scorers. By rehearsing potential rebuttals and defenses before the debate begins, TreeDebater can estimate the robustness of its claims from multiple perspectives.

Debate Flow Tree: This tree acts as a dynamic record of the debate as it unfolds. It captures the evolving status of arguments, integrating node details with information on whether claims are proposed, attacked, or rebutted. By maintaining the debate's flow structure, the AI can strategically plan its next move and retrieve prepared arguments from the rehearsal tree that are most suitable for the debate's current state.

Strategic Planning with Decision Trees: Using these trees, TreeDebater intelligently allocates its limited speaking time. The simulated audience feedback and speech time controller further refine this process, ensuring that outputs align well with human expectations and the formal time constraints of a debate setting.

Numerical Results and Evaluation

Extensive human evaluations reveal the efficacy of TreeDebater compared to the state-of-the-art multi-agent debate system, Agent4Debate. Empirical results demonstrate a consistent preference for TreeDebater at both the stage-level and debate-level comparisons. In controlled settings, TreeDebater showcases improved persuasiveness scores across all debate stages, affirming its strategic advantages. Notably, TreeDebater's architecture facilitates more diverse argumentation strategies, aligning more closely with human expert techniques observed in real-world debate competitions.

Theoretical and Practical Implications

TreeDebater's ability to navigate the complex dynamics of competitive debate highlights important theoretical contributions to AI decision-making frameworks. By modeling debate interactions as tree structures akin to strategic planning in games like Chess or Go, TreeDebater advances our understanding of LLM capabilities in non-deterministic environments. Practically, these developments could extend to broader applications in negotiation, legal discourse analysis, and other areas requiring sophisticated argumentation and reasoning.

Future Developments

Looking ahead, the integration of more nuanced scoring models and increased computational power for processing tree structures could further elevate TreeDebater's performance. Additionally, continued exploration of multi-agent frameworks and simulated audience feedback models may yield improvements in AI-human interaction paradigms, reinforcing AI's role in assisting and enhancing human decision-making processes.

The paper meticulously substantiates its claims through comprehensive experimental and analytical processes, contributing robustly to the discourse on enhancing argumentative capabilities in AI systems. As LLMs continue to evolve, approaches like TreeDebater represent promising avenues for not only elevating AI performance in competitive settings but also enhancing AI's strategic thinking and reasoning capacities more fundamentally.