@inproceedings{sen-etal-2023-knowledge,
    title = "Knowledge Graph-augmented Language Models for Complex Question Answering",
    author = "Sen, Priyanka  and
      Mavadia, Sandeep  and
      Saffari, Amir",
    editor = "Dalvi Mishra, Bhavana  and
      Durrett, Greg  and
      Jansen, Peter  and
      Neves Ribeiro, Danilo  and
      Wei, Jason",
    booktitle = "Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)",
    month = jun,
    year = "2023",
    address = "Toronto, Canada",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2023.nlrse-1.1",
    doi = "10.18653/v1/2023.nlrse-1.1",
    pages = "1--8",
    abstract = "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.",
}

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.
Sen, Priyanka Mavadia, S eep Saffari, Amir
Knowledge Graph-augmented Language Models for Complex Question Answering