Knowledge Graph-augmented Language Models for Complex Question Answering
Priyanka Sen, Sandeep Mavadia, Amir Saffari · 2023
Large language models have shown impressive abilities to reason over input text, however, they are prone to hallucinations.On the other hand, end-to-end knowledge graph question answering (KGQA) models output responses grounded in facts, but they still struggle with complex reasoning, such as comparison or ordinal questions.In this paper, we propose a new method for complex question answering where we combine a knowledge graph retriever based on an end-to-end KGQA model with a language model that reasons over the retrieved facts to return an answer.We observe that augmenting language model prompts with retrieved KG facts improves performance over using a language model alone by an average of 83%.In particular, we see improvements on complex questions requiring count, intersection, or multi-hop reasoning operations.