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Political Events using RAG with LLMs

arXiv 2502.15701 ↗First posted 6 Jan 2025cs.IR cs.AI cs.CLcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 44 references that are in the map. First posted on arXiv 6 Jan 2025.

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15 of 44 references.

References · 15

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3 more
  1. 2017Protest Activity Detection and Perceived Violence Estimation from Social Media Imagescited by 20
  2. 2024Improving Assessment of Tutoring Practices using Retrieval-Augmented Generationcited by 11
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  7. 2024A Comprehensive Evaluation of Large Language Models on Temporal Event Forecasting
  8. 2025Retrieval Augmented Generation Evaluation in the Era of Large Language Models: A Comprehensive Survey