Large language model-based querying utilizes LLMs as natural language interfaces to extract and retrieve structured information, often from knowledge graphs or unstructured text. It aims to make insights from narrative content machine-actionable, complementing traditional symbolic querying methods.
Large language model-based querying allows people to ask questions in plain English to get information from complex data, including scientific papers. It helps unlock insights stuck in text and tables, making them useful for computers, but works best when combined with reliable, structured data systems.
LLM querying, natural language querying (LLM-powered), neurosymbolic querying (LLM component)
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