We discuss the integration of LLMs with Knowledge Graphs (KGs) to ground their reasoning capabilities. It highlights the importance of KGs in providing structured knowledge that is interpretable and computationally efficient. The integration of KGs with LLMs helps in reducing hallucinations and ensuring that the generated outputs are grounded in verifiable data.
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LLMs tend to be overconfident, always providing an answer to the user's query, even when the model is unsure about the answer. What happens when we ask the LLMs questions for which the answer is unclear even to humans?
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LLMs tend to be overconfident, always providing an answer to the user's query, even when the model is unsure about the answer. What happens when we ask the LLMs questions for which the answer is unclear even to humans?
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In this work, we explore and try to improve how we can answer complex logical queries over knowledge graphs. We call them complex logical queries because the questions can be decomposed into multiple logical statements to reach the final answer.
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