Large Language Models Meet Knowledge Graphs to Answer Factoid Questions
Published in Pacific Asia Conference on Language, Information and Computation (PACLIC 37), 2023
Recommended citation: Salnikov et. al (2023). "Large Language Models Meet Knowledge Graphs to Answer Factoid Questions" In proceedings of Pacific Asia Conference on Language, Information and Computation (PACLIC 2023). https://arxiv.org/abs/2310.02166
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%.
Recommended citation: Salnikov et. al (2023). “Large Language Models Meet Knowledge Graphs to Answer Factoid Questions” In proceedings of Pacific Asia Conference on Language, Information and Computation (PACLIC 2023).