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            <namePart type="given">Bhavana</namePart>
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    <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.</abstract>
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%0 Conference Proceedings
%T Knowledge Graph-augmented Language Models for Complex Question Answering
%A Sen, Priyanka
%A Mavadia, Sandeep
%A Saffari, Amir
%Y Dalvi Mishra, Bhavana
%Y Durrett, Greg
%Y Jansen, Peter
%Y Neves Ribeiro, Danilo
%Y Wei, Jason
%S Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)
%D 2023
%8 June
%I Association for Computational Linguistics
%C Toronto, Canada
%F sen-etal-2023-knowledge
%X 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.
%R 10.18653/v1/2023.nlrse-1.1
%U https://aclanthology.org/2023.nlrse-1.1
%U https://doi.org/10.18653/v1/2023.nlrse-1.1
%P 1-8

Markdown (Informal)

[Knowledge Graph-augmented Language Models for Complex Question Answering](https://aclanthology.org/2023.nlrse-1.1) (Sen et al., NLRSE 2023)

• Knowledge Graph-augmented Language Models for Complex Question Answering (Sen et al., NLRSE 2023)

ACL

• Priyanka Sen, Sandeep Mavadia, and Amir Saffari. 2023. Knowledge Graph-augmented Language Models for Complex Question Answering. In Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE), pages 1–8, Toronto, Canada. Association for Computational Linguistics.