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UID:pretalx-pyconde-pydata-2024-LWWQ9U@pretalx.com
DTSTART;TZID=CET:20240422T161000
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DESCRIPTION:Large Language Models (LLMs) have proven to be incredibly power
 ful on a range of tasks. They do however\, have certain limitations when t
 he input context becomes significantly large. Solutions such as Retrieval 
 Augmented Generation (RAG) do a great job in providing context from custom
  data without retraining any models but they too have limitations\, especi
 ally when the context is spread out over many documents. Consider the ques
 tion “Which projects has person X worked on?”. Information required to
  answer this question may be spread out over hundreds of documents\, makin
 g it difficult for an LLM alone to answer. One way to overcome this issue 
 is to use an LLM as an entity extraction tool\, which can extract entities
  and relationships from documents and load that data into a structured for
 mat such as a knowledge graph. In this talk\, I will demonstrate this proc
 ess on a dataset of parliamentary debates\, showing how downstream analyti
 cs becomes more intuitive and feasible.
DTSTAMP:20260717T015935Z
LOCATION:B05-B06
SUMMARY:Using LLMs to Create Knowledge Graphs From a Large Corpus of Parlia
 mentary Debates - Usman
URL:https://pretalx.com/pyconde-pydata-2024/talk/LWWQ9U/
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