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Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
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This method proposes a novel four-stage pipeline that dynamically constructs and expands knowledge graphs (KGs) during the LLM's inference process. The KG is built by extracting initial triplets from the question, iteratively expanding them using the LLM's internal knowledge, and refining them through external retrieval from sources like Wikipedia and Google Search. The ultimate goal is to improve the factual accuracy and robustness of LLM-generated answers in Question Answering tasks.
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