GateNLP在SemEval-2025任务10中：用于多语言叙事分类的分层三步提示
GateNLP at SemEval-2025 Task 10: Hierarchical Three-Step Prompting for Multilingual Narrative Classification
AI总结：
本文提出用于多语言叙事分类的分层三步提示（H3Prompt）方法，通过三步LLM提示策略将新闻文章分类至两级分类体系，并在SemEval 2025任务10的英语测试集中获得全球第一。
摘要：
在线新闻的激增和虚假信息的日益传播需要强大的自动数据分析方法。叙事分类正成为一项重要任务，因为识别在线内容中所述内容对于事实核查人员、政策制定者及其他从事信息研究的专业人员至关重要。本文介绍了我们针对SemEval 2025任务10子任务2的方法，该方法旨在将新闻文章分类为预定义的包含主叙事和子叙事的两级分类体系，涵盖多种语言。我们提出了用于多语言叙事分类的分层三步提示（H3Prompt）。我们的方法遵循三步大型语言模型（LLM）提示策略，模型首先将文章归类为两个领域（乌克兰-俄罗斯战争或气候变化）之一，然后识别最相关的主叙事，最后分配子叙事。我们的方法在全球28支参赛队伍中获得了英语测试集的最高排名。代码可在https://github.com/GateNLP/H3Prompt获取。
英文摘要：
The proliferation of online news and the increasing spread of misinformation necessitate robust methods for automatic data analysis. Narrative classification is emerging as a important task, since identifying what is being said online is critical for fact-checkers, policy markers and other professionals working on information studies. This paper presents our approach to SemEval 2025 Task 10 Subtask 2, which aims to classify news articles into a pre-defined two-level taxonomy of main narratives and sub-narratives across multiple languages. We propose Hierarchical Three-Step Prompting (H3Prompt) for multilingual narrative classification. Our methodology follows a three-step Large Language Model (LLM) prompting strategy, where the model first categorises an article into one of two domains (Ukraine-Russia War or Climate Change), then identifies the most relevant main narratives, and finally assigns sub-narratives. Our approach secured the top position on the English test set among 28 competing teams worldwide. The code is available at https://github.com/GateNLP/H3Prompt.
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。
