利用大型语言模型和说服策略检测获胜论点
Detecting Winning Arguments with Large Language Models and Persuasion Strategies
AI总结：
本文利用大型语言模型和说服策略分析论证文本，通过多策略评分方法提升说服力预测，并公开发布主题注释数据集以促进研究。
摘要：
检测论证中的说服性是一个具有重要影响的任务，对于理解人类交流至关重要。本文研究了说服策略（如攻击声誉、转移注意力和操纵用语）在确定文本说服力中的作用。我们在三个注释的论证数据集上进行实验：Winning Arguments（源自Change My View子论坛）、Anthropic/Persuasion和Persuasion for Good。我们的方法利用大型语言模型（LLMs），采用多策略说服评分方法，引导对六个说服策略的推理。结果表明，策略引导的推理提高了说服力的预测能力。为了更好地理解内容的影响，我们将Winning Argument数据集按广泛讨论的主题组织，并分析其在不同主题上的表现。我们公开发布这个主题注释的版本，以促进未来研究。总体而言，我们的方法展示了结构化、策略感知提示在提高论证质量评估的可解释性和鲁棒性方面的价值。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
