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Knowledge Graph Insights

Fran Alexander: Alien vs Predator and LLMs vs Knowledge Graphs – Episode 15

Dec 7, 2024
In this discussion, Fran Alexander, an independent taxonomist and ontologist, draws fascinating parallels between the Alien vs. Predator franchise and the realms of LLMs and knowledge graphs. She explores how knowledge graphs offer structured, predictable frameworks, while LLMs are unpredictable and complex. Fran highlights the issues of bias and transparency in LLMs and the importance of combining their strengths with knowledge graphs for enhanced AI outcomes. Ultimately, she emphasizes how taxonomists can harness LLMs in decision-making and taxonomy building.
34:55

Episode guests

Podcast summary created with Snipd AI

Quick takeaways

  • Fran Alexander uses the Alien vs Predator metaphor to illustrate how knowledge graphs are precise and reliable, while LLMs are unpredictable and stochastic.
  • The integration of knowledge graphs and LLMs through architectures like retrieval augmented generation enhances accuracy by leveraging the strengths of both technologies.

Deep dives

Combining Strengths of Knowledge Graphs and LLMs

The intersection of knowledge graphs and large language models (LLMs) creates powerful new architectures, such as retrieval augmented generation, which enhance both technologies. Knowledge graphs provide a structured and reliable source of information, while LLMs excel in generating natural language text. This symbiotic relationship allows for systems that leverage the specificity and precision of knowledge graphs to ground the more fluid and creative outputs produced by LLMs. By merging these tools, users can harness the strengths of both, resulting in enhanced accuracy and value.

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