用大模型+检索增强，自动从新闻中提取政治事件。
Political Events using RAG with LLMs
- 结合大模型与外部数据检索，提升事件抽取能力。
- 无需大量标注数据，可快速部署于政治新闻分析。
- 适合关注时政动态的媒体、研究者和政策分析人员。
在当今数字环境中，媒体内容是政治新闻分析的基础，涵盖新闻文章、社交媒体动态、演讲和报告等多渠道信息。自然语言处理（NLP）已推动政治信息抽取（IE）的自动化，尤其在从各类媒体中提取事件（EE）方面取得进展。传统NLP方法需依赖专业规则或特定领域数据训练模型，而大型语言模型（LLMs）结合生成式人工智能（GenAI）提供了更易用的替代方案，降低模型构建门槛并减少对大规模训练数据的依赖，实现快速部署。然而，面对领域特定任务仍存在挑战，因此引入了检索增强生成（RAG）框架。RAG通过整合外部数据检索，扩展模型上下文理解与知识范围，超越预训练数据限制。本文提出面向新闻文章的政治事件抽取系统，有效提取政治事件信息，有助于及时掌握国内外政治动态。
原文摘要 · Abstract (English)
In the contemporary digital landscape, media content stands as the foundation for political news analysis, offering invaluable insights sourced from various channels like news articles, social media updates, speeches, and reports. Natural Language Processing (NLP) has revolutionized Political Information Extraction (IE), automating tasks such as Event Extraction (EE) from these diverse media outlets. While traditional NLP methods often necessitate specialized expertise to build rule-based systems or train machine learning models with domain-specific datasets, the emergence of Large Language Models (LLMs) driven by Generative Artificial Intelligence (GenAI) presents a promising alternative. These models offer accessibility, alleviating challenges associated with model construction from scratch and reducing the dependency on extensive datasets during the training phase, thus facilitating rapid implementation. However, challenges persist in handling domain-specific tasks, leading to the development of the Retrieval-Augmented Generation (RAG) framework. RAG enhances LLMs by integrating external data retrieval, enriching their contextual understanding, and expanding their knowledge base beyond pre-existing training data. To illustrate RAG's efficacy, we introduce the Political EE system, specifically tailored to extract political event information from news articles. Understanding these political insights is essential for remaining informed about the latest political advancements, whether on a national or global scale.
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