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%.

翻译：近期研究表明，将结构化知识融入大语言模型可显著提升多种自然语言处理任务的效果。本文提出一种方法，利用知识图谱中的附加信息增强预训练文本到文本语言模型，用于回答事实性问题。具体而言，我们设计了一种基于问题实体和候选答案从知识图谱中提取子图的算法，随后通过线性化提取的子图，利用基于Transformer的模型获取易于解释的信息。最终使用提取的信息对候选答案进行重排序，使预训练文本到文本语言模型的Hits@1得分提升4-6%。