Evaluating GraphRAG’s Role in Improving Contextual Understanding of News in Newsrooms.

Abstract

In a newsroom, journalists are frequently tasked with re-
porting on complex events, such as conflicts, where understanding the
broader context and nuanced details is crucial for accurate and insightful
reporting. The challenge lies in processing and synthesizing vast amounts
of information from various sources to build a comprehensive picture of
the event. This requires not only retrieving specific facts but also under-
standing the interconnections between different pieces of information.The
advent of Large Language Models (LLMs) has brought advancements in
text processing, offering the capability to quickly retrieve and generate
content from extensive datasets. However, the use of LLMs in newsrooms
comes with challenges, particularly concerning their static knowledge
and hallucination, where models produce responses that are plausible
but incorrect. To address these challenges, Retrieval-Augmented Gener-
ation (RAG) has been developed, to provide responses grounded in actual
data. RAG is effective for straightforward queries where the information
is contained within specific documents. However, it has limitations when
dealing with complex queries that involve synthesizing information from
multiple 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 sources
in a structured manner. In this work, we design a set of experiments to
compare GraphRAG’s capabilities to that of previous general LLM and
RAG based approaches’, when it comes to understanding and accurately
representing complex issues.