Year: 2024
Type: article
Abstract: In a newsroom, journalists are frequently tasked with re-porting on complex events, such as conflicts, where understanding thebroader context and nuanced details is crucial for accurate and insightfulreporting. The challenge lies in processing and synthesizing vast amountsof information from various sources to build a comprehensive picture ofthe event. This requires not only retrieving specific facts but also under-standing the interconnections between different pieces of information.Theadvent of Large Language Models (LLMs) has brought advancements intext processing, offering the capability to quickly retrieve and generatecontent from extensive datasets. However, the use of LLMs in newsroomscomes with challenges, particularly concerning their static knowledgeand hallucination, where models produce responses that are plausiblebut incorrect. To address these challenges, Retrieval-Augmented Gener-ation (RAG) has been developed, to provide responses grounded in actualdata. RAG is effective for straightforward queries where the informationis contained within specific documents. However, it has limitations whendealing with complex queries that involve synthesizing information frommultiple sources or understanding intricate relationships between enti-ties. GraphRAG offers to overcome such limitations by leveraging knowl-edge graphs, which offer to combine information from multiple sourcesin a structured manner. In this work, we design a set of experiments tocompare GraphRAG’s capabilities to that of previous general LLM andRAG based approaches’, when it comes to understanding and accuratelyrepresenting complex issues.
DOI: 10.5324/nikt.6231
Institution: University of Bergen
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4408171998