结合RAG与大语言模型的政治事件研究
Political Events using RAG with LLMs
AI总结：
针对大语言模型处理政治领域事件抽取任务的局限，本研究引入RAG框架增强模型能力，开发了专门从新闻文章中抽取政治事件信息的系统，为政治新闻分析提供高效易用的技术方案。
摘要：
在当今数字环境中，媒体内容是政治新闻分析的基础，可从新闻文章、社交媒体动态、演讲及报告等各类渠道提供极具价值的洞察。自然语言处理（NLP）已革新了政治信息抽取（IE）领域，实现了从这些多元媒体渠道中开展事件抽取（EE）等任务的自动化。传统NLP方法往往需要专业知识来构建基于规则的系统，或使用领域特定数据集训练机器学习模型，而由生成式人工智能（GenAI）驱动的大语言模型（LLMs）的出现提供了一种颇具前景的替代方案。这类模型具备易用性，缓解了从零构建模型的相关难题，还降低了训练阶段对大规模数据集的依赖，从而便于快速落地部署。但这类模型在处理领域特定任务时仍存在挑战，由此催生了检索增强生成（RAG）框架的开发。RAG通过整合外部数据检索功能增强LLMs，丰富其上下文理解能力，并将其知识库拓展至预存训练数据之外。为验证RAG的有效性，我们推出了政治事件抽取系统，该系统专门用于从新闻文章中抽取政治事件信息。无论是国家层面还是全球层面的政治动态，理解这些政治洞察对于及时了解最新政治进展都至关重要。
英文摘要：
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.
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。
