让AI写议论文更讲逻辑，用树状结构自动补全论证链条
Prove Your Point!: Bringing Proof-Enhancement Principles to Argumentative Essay Generation
- 用大模型生成逻辑伪标签，再通过树形规划确保论点间连贯
- 在10个争议话题上，逻辑正确率比基线高17.3%，说服力更强
- 适合需要严谨论证的写作场景，如学术写作、辩论训练
议论文生成（AEG）旨在针对特定争议话题生成完整文本。现有方法虽能生成单个观点，但常忽视观点间的高层级关联，导致生成内容逻辑混乱，无法有效自证。例如，论据与主张矛盾，或论点无法形成连贯推导流。本文提出统一的两阶段框架：证明增强与自标注（PESA），聚焦逻辑强化。首先利用大语言模型构建论点、论据等逻辑信息的伪标签；随后设计树状规划方法，引入证明原则以保证逻辑一致性。大量实验表明，得益于证明原则引导，PESA生成的议论文在逻辑有效性与说服力上均优于多个强基线模型。
原文摘要 · Abstract (English)
Argumentative essay generation (AEG) aims to generate complete texts on specific controversial topics or debates. Although current AEG methods can generate individual opinions, they often overlook the high-level connections between these opinions. This often leads to the generated results being mired in logical confusion, unable to proof their own arguments effectively. The generated essay may present evidence that contradicts the claims or they may fail to assemble the claims into logical flow. In this paper, we present a unified two-stage framework: Proof-Enhancement and Self-Annotation (PESA) for AEG with a focus on logical enhancement. Specifically, we first construct pseudo-labels for logical information,claims and grounds, using a large language model. We then propose a tree planning approach that introduces proof principles and ensures logical consistency. Extensive experimental results show that, benefiting from proof principle guidance, PESA generates argumentative essays with better logical validity and persuasiveness than strong baseline models.
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