# Barack's Wife Hillary: Using Knowledge-Graphs for Fact-Aware Language Modeling

## TL;DR

This paper introduces the KGLM, a neural language model that integrates knowledge graphs to improve factual accuracy and recall of unseen facts, outperforming traditional models and large language models.

## Contribution

The paper presents the KGLM, a novel language model with mechanisms for selecting and copying facts from knowledge graphs, and introduces the Linked WikiText-2 dataset for training and evaluation.

## Key findings

- KGLM significantly outperforms baseline language models.
- KGLM surpasses large language models in factual sentence completion.
- The Linked WikiText-2 dataset aligns text with Wikidata for improved factual modeling.

## Abstract

Modeling human language requires the ability to not only generate fluent text but also encode factual knowledge. However, traditional language models are only capable of remembering facts seen at training time, and often have difficulty recalling them. To address this, we introduce the knowledge graph language model (KGLM), a neural language model with mechanisms for selecting and copying facts from a knowledge graph that are relevant to the context. These mechanisms enable the model to render information it has never seen before, as well as generate out-of-vocabulary tokens. We also introduce the Linked WikiText-2 dataset, a corpus of annotated text aligned to the Wikidata knowledge graph whose contents (roughly) match the popular WikiText-2 benchmark. In experiments, we demonstrate that the KGLM achieves significantly better performance than a strong baseline language model. We additionally compare different language model's ability to complete sentences requiring factual knowledge, showing that the KGLM outperforms even very large language models in generating facts.