Enhancing Large Language Models with Pseudo- and Multisource- Knowledge
  Graphs for Open-ended Question Answering
Jiaxiang Liu Tong Zhou Yubo Chen Kang Liu Jun Zhao
Mitigating the hallucinations of Large Language Models (LLMs) and enhancing
them is a crucial task. Although some existing methods employ model
self-enhancement techniques, they fall short of effectively addressing unknown
factual hallucinations. Using Knowledge Graph (KG) enhancement approaches fails
to address the generalization across different KG sources and the enhancement
of open-ended answer questions simultaneously. To tackle these limitations,
there is a framework that combines Pseudo-Graph Generation and Atomic Knowledge
Verification proposed. The enhancement of LLM using KG in an open-ended
question-answering setting is implemented by leveraging the Pseudo-Graph
Generation. Atomic Knowledge Verification utilizes atomic-level knowledge
querying and verification to achieve generalizability under different KG
sources. Compared to the baseline, this approach yields a minimum improvement
of 11.5 in the ROUGE-L score for open-ended questions. For precise questions,
we observe a minimum accuracy improvement of 7.5. Moreover, there is also
demonstration that this framework exhibits generalizability across different KG
sources. In summary, our results pave the way for enhancing LLMs by
incorporating Pseudo- and Multisource-KGs, particularly in the context of
open-ended questions.