Knowledge Graphs for Trustworthy LLM Question Answering
Description
https://www.sciencedirect.com/science/article/pii/S1570826824000441
This pre-print research paper
investigates the use of knowledge graphs to improve the accuracy and
trustworthiness of Large Language Model (LLM)-powered question answering
systems in enterprise settings. The authors argue
that knowledge graphs provide a crucial framework for validating
LLM-generated queries, explaining results, and ensuring access to
reliable data. Their research includes a benchmark study demonstrating the accuracy improvements achieved by incorporating knowledge graphs. The paper also explores lessons learned regarding knowledge engineering, explainability, governance, and effective question selection strategies. Finally, it outlines key industry needs and future research directions in this area.
United States
