Integrating Knowledge Graphs and Vector RAG for Efficient Information Extraction - Reading Group Session

Integrating Knowledge Graphs and Vector RAG for Efficient Information Extraction - Reading Group Session

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[] Consider separating entity attributes from relationships

14 of 16

14 of 16

[] Consider separating entity attributes from relationships

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Integrating Knowledge Graphs and Vector RAG for Efficient Information Extraction - Reading Group Session

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  1. 1 [] Hybrid rank combines knowledge graphs, vector retrieval
  2. 2 [] Hybrid drag improves accuracy using knowledge graphs
  3. 3 [] Text preprocessing and graph algorithms improve LLM responses
  4. 4 [] Faithfulness measures the consistency of LLM-generated answers
  5. 5 [] Faithfulness metric should normalize statements' word counts
  6. 6 [] Analyzed Nifty 50 earning calls for hedge fund
  7. 7 [] Construct a knowledge graph and chunk text data
  8. 8 [] Example of earnings extraction illustrating graph advantages
  9. 9 [] Traversing graph to find relevant entities' relationships
  10. 10 [] Highly connected graph; combine vectors, graphs arbitrarily
  11. 11 [] Graph neural networks improve re-ranking and retrieval
  12. 12 [] Comparison methods are unfair due to unequal context size
  13. 13 [] Making larger language models traverse graph structures
  14. 14 [] Consider separating entity attributes from relationships
  15. 15 [] Join discussions on Slack, suggest topics, participate
  16. 16 [] Wrap up

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