Large Language Models Meet Knowledge Graphs to Answer Factoid Questions
Mikhail Salnikov Hai Le Prateek Rajput Irina Nikishina Pavel Braslavski Valentin Malykh Alexander Panchenko
Recently, it has been shown that the incorporation of structured knowledge
into Large Language Models significantly improves the results for a variety of
NLP tasks. In this paper, we propose a method for exploring pre-trained
Text-to-Text Language Models enriched with additional information from
Knowledge Graphs for answering factoid questions. More specifically, we propose
an algorithm for subgraphs extraction from a Knowledge Graph based on question
entities and answer candidates. Then, we procure easily interpreted information
with Transformer-based models through the linearization of the extracted
subgraphs. Final re-ranking of the answer candidates with the extracted
information boosts Hits@1 scores of the pre-trained text-to-text language
models by 4-6%.