用大模型分析说服策略，提升判断论点说服力的准确性。
Detecting Winning Arguments with Large Language Models and Persuasion Strategies
- 设计六种说服策略引导大模型推理
- 在三个数据集上显著提升说服力预测效果
- 公开主题标注版数据集，助力后续研究
检测论证文本中的说服力是一项具有重要意义但极具挑战的任务。本文研究了攻击声誉、转移注意力、操纵性措辞等说服策略对文本说服力的影响。在三个标注的论证数据集——Winning Arguments（源自Change My View subreddit）、Anthropic/Persuasion和Persuasion for Good上进行实验，提出基于大语言模型的多策略说服评分方法，通过引导模型对六类说服策略进行推理。结果表明，策略引导的推理能有效提升说服力预测性能。为进一步理解内容影响，将Winning Arguments数据集按讨论主题分类，并分析各主题上的表现。本文公开发布该主题标注版本的数据集，以促进未来研究。整体方法展示了结构化、策略感知提示在提升论证质量评估可解释性与鲁棒性方面的价值。
原文摘要 · Abstract (English)
Detecting persuasion in argumentative text is a challenging task with important implications for understanding human communication. This work investigates the role of persuasion strategies - such as Attack on reputation, Distraction, and Manipulative wording - in determining the persuasiveness of a text. We conduct experiments on three annotated argument datasets: Winning Arguments (built from the Change My View subreddit), Anthropic/Persuasion, and Persuasion for Good. Our approach leverages large language models (LLMs) with a Multi-Strategy Persuasion Scoring approach that guides reasoning over six persuasion strategies. Results show that strategy-guided reasoning improves the prediction of persuasiveness. To better understand the influence of content, we organize the Winning Argument dataset into broad discussion topics and analyze performance across them. We publicly release this topic-annotated version of the dataset to facilitate future research. Overall, our methodology demonstrates the value of structured, strategy-aware prompting for enhancing interpretability and robustness in argument quality assessment.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。