arXiv 2409.168072024-09-25cs.CL

少数伪君子：在线气候变化辩论中伪善指控检测的少样本学习与子类型定义

A Few Hypocrites: Few-Shot Learning and Subtype Definitions for Detecting Hypocrisy Accusations in Online Climate Change Debates

Hertie School（赫蒂学院）；Hebrew University of Jerusalem（耶路撒冷希伯来大学）；University of Hamburg（汉堡大学）；Ilmenau University of Technology（伊尔默瑙工业大学）；University of Zurich（苏黎世大学）；Vrije Universiteit Amsterdam（阿姆斯特丹自由大学）

Paulina Garcia Corral, Avishai Green, Hendrik Meyer, Anke Stoll, Xiaoyue Yan, Myrthe Reuver

备注：: cite the public version, published at CPSS 2024 @ KONVENS

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AI总结：

本文将伪善指控检测定义为NLP独立任务，构建专家标注的CHAC数据集，并评估少样本上下文学习及指令微调LLMs，发现GPT-4o和Llama-3表现优异，但在识别政治伪善上仍有挑战。

摘要：

气候危机是网络讨论中的突出问题，而伪善指控是这些辩论中的核心修辞元素。然而，对于大规模文本分析而言，伪善指控检测是一个研究不足的工具，通常被定义为谬误论证检测的较小子任务。在本文中，我们将伪善指控检测定义为NLP中的一项独立任务，并识别了伪善指控的不同相关子类型。我们的Climate Hypocrisy Accusation Corpus (CHAC) 包含420条Reddit气候辩论评论，由专家标注为两种不同类型的伪善指控：个人伪善与政治伪善。我们评估了使用6个样本的少样本上下文学习以及3个指令微调的大型语言模型（LLMs）在该数据集中检测伪善指控的表现。结果表明，GPT-4o和Llama-3模型在检测伪善指控方面表现突出（F1达到0.68，而先前工作的F1为0.44）。然而，对于伪善指控这类复杂的语义概念，上下文至关重要，我们发现与个人道德伪善相比，模型在识别政治伪善指控方面尤为困难。我们的研究为伪善检测和气候变化话语提供了新见解，并为在线气候辩论中伪善指控的大规模分析奠定了基础。

英文摘要：

The climate crisis is a salient issue in online discussions, and hypocrisy accusations are a central rhetorical element in these debates. However, for large-scale text analysis, hypocrisy accusation detection is an understudied tool, most often defined as a smaller subtask of fallacious argument detection. In this paper, we define hypocrisy accusation detection as an independent task in NLP, and identify different relevant subtypes of hypocrisy accusations. Our Climate Hypocrisy Accusation Corpus (CHAC) consists of 420 Reddit climate debate comments, expert-annotated into two different types of hypocrisy accusations: personal versus political hypocrisy. We evaluate few-shot in-context learning with 6 shots and 3 instruction-tuned Large Language Models (LLMs) for detecting hypocrisy accusations in this dataset. Results indicate that the GPT-4o and Llama-3 models in particular show promise in detecting hypocrisy accusations (F1 reaching 0.68, while previous work shows F1 of 0.44). However, context matters for a complex semantic concept such as hypocrisy accusations, and we find models struggle especially at identifying political hypocrisy accusations compared to personal moral hypocrisy. Our study contributes new insights in hypocrisy detection and climate change discourse, and is a stepping stone for large-scale analysis of hypocrisy accusation in online climate debates.

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。