用大模型生成数据，让讽刺文本更均衡，提升检测模型泛化能力。
Make Satire Boring Again: Reducing Stylistic Bias of Satirical Corpus by Utilizing Generative LLMs
- 用生成式大模型扩充并平衡讽刺语料，减少风格偏差。
- 在跨领域和跨语言场景下，模型检测准确率显著提升。
- 适用于需要公平性与可解释性的讽刺识别任务研究者。
讽刺检测对从文本中准确提取观点、应对网络虚假信息至关重要。然而，讽刺语料库多样性不足导致风格偏差，影响模型性能。本文提出一种基于生成式大语言模型的去偏方法，旨在通过生成数据降低训练集中的风格偏差。该方法在跨领域（反语检测）和跨语言（英语）设置下进行了评估，结果表明其能有效提升土耳其语和英语环境下讽刺与反语检测模型的鲁棒性与泛化能力。但对因果语言模型如 Llama-3.1 的效果有限。此外，本研究还构建了带有详细人工标注的土耳其讽刺新闻数据集，并开展分类、去偏与可解释性案例研究。
原文摘要 · Abstract (English)
Satire detection is essential for accurately extracting opinions from textual data and combating misinformation online. However, the lack of diverse corpora for satire leads to the problem of stylistic bias which impacts the models' detection performances. This study proposes a debiasing approach for satire detection, focusing on reducing biases in training data by utilizing generative large language models. The approach is evaluated in both cross-domain (irony detection) and cross-lingual (English) settings. Results show that the debiasing method enhances the robustness and generalizability of the models for satire and irony detection tasks in Turkish and English. However, its impact on causal language models, such as Llama-3.1, is limited. Additionally, this work curates and presents the Turkish Satirical News Dataset with detailed human annotations, with case studies on classification, debiasing, and explainability.
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