用分层推理树让大模型的判断过程可解释、可争议，提升可信度。
ART: Adaptive Reasoning Trees for Explainable Claim Verification
- 构建分层推理树，通过裁判模型逐级比对论点强弱
- 在多个数据集上优于主流基线，验证了结构化推理的有效性
- 适合需要透明决策的高风险场景，如司法、医疗判断
大型语言模型（LLMs）在复杂决策中表现强大，具备海量知识和零样本能力。然而其在高风险环境中的应用受限于黑箱特性：输出缺乏可信解释，错误难以被挑战纠正，削弱了可信度。本文提出ART（自适应推理树），一种用于声明验证的分层方法。从根声明出发，分支为支持与反驳的子论点；论点强度通过下至上进行两两锦标赛式比较，由裁判模型判定，从而系统生成透明且可争议的最终结论。该机制弥补了链式思维（CoT）等方法在可解释性上的缺失。我们在多个数据集上验证ART，分析不同论点生成器与对比策略的效果。结果表明，结构化推理显著优于强基线，确立了可解释声明验证的新基准，提升了决策过程的可靠性与清晰度。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are powerful candidates for complex decision-making, leveraging vast encoded knowledge and remarkable zero-shot abilities. However, their adoption in high-stakes environments is hindered by their opacity; their outputs lack faithful explanations and cannot be effectively contested to correct errors, undermining trustworthiness. In this paper, we propose ART (Adaptive Reasoning Trees), a hierarchical method for claim verification. The process begins with a root claim, which branches into supporting and attacking child arguments. An argument's strength is determined bottom-up via a pairwise tournament of its children, adjudicated by a judge LLM, allowing a final, transparent and contestable verdict to be systematically derived which is missing in methods like Chain-of-Thought (CoT). We empirically validate ART on multiple datasets, analyzing different argument generators and comparison strategies. Our findings show that ART's structured reasoning outperforms strong baselines, establishing a new benchmark for explainable claim verification which is more reliable and ensures clarity in the overall decision making step.
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