用试辩机制提升论说文理解的准确率。
Towards Robust Argumentative Essay Understanding via TIDE: An Interactive Framework with Trial and Debate
- 引入试辩机制优化基于标准的提示词
- 在三类任务上均提升性能，效果稳定
- 适合需要深度推理评估的研究者
论说文是评估批判性思维与推理能力的重要载体，但现有研究在通过提示词精准理解与评价此类文本方面仍显不足。本文提出TIDE框架，通过整合试辩（TrIal and DEbate）机制，改进基于标准的提示词优化方法。该方法有效缓解了噪声训练数据的影响，提升了优化过程的稳定性。我们在自动作文评分、论点成分识别和论点关系识别三个核心任务上验证了TIDE的有效性。实验结果表明，该框架在各项任务中均实现性能提升，证明了结合提示方法进行高级论说文理解的潜力。
原文摘要 · Abstract (English)
Argumentative essays serve as a vital medium for assessing critical thinking and reasoning skills, yet there is limited works on accurately understanding and evaluating such texts via prompt. In this work, we propose TIDE, a novel framework designed to improve criteria-based prompt optimization for argument-related tasks by integrating TrIal and DEbate mechanism. Our method addresses key limitations of criteria-based prompt optimizing by mitigating the influence of noisy training data and enhancing optimization stability. We evaluate TIDE on three core tasks: Automated Essay Scoring, Argument Component Detection, and Argument Relation Identification. Results demonstrate that our framework improves performance across tasks. These findings underscore the potential of combining prompt-based methods for advanced argument understanding.
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