Political Events using RAG with LLMs
Muhammad Arslan (Le2i, ICB), Saba Munawar (NUCES), Christophe Cruz, (ICB)

TL;DR
This paper explores using Retrieval-Augmented Generation with Large Language Models to improve political event extraction from diverse media sources, demonstrating a novel system tailored for political news analysis.
Contribution
It introduces a Political Event Extraction system leveraging RAG with LLMs, addressing domain-specific challenges and enhancing extraction accuracy from news articles.
Findings
RAG improves political event extraction accuracy.
The system effectively integrates external data for contextual understanding.
Enhanced political insight extraction from media sources.
Abstract
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…
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Taxonomy
TopicsAdvanced Text Analysis Techniques · Topic Modeling · Natural Language Processing Techniques
MethodsAttention Is All You Need · Weight Decay · Refunds@Expedia|||How do I get a full refund from Expedia? · Linear Layer · Layer Normalization · Byte Pair Encoding · WordPiece · Dense Connections · Attention Dropout · Residual Connection
