Language Model Integration

Jerry discusses the evolution of integrating external data into language models, highlighting the challenges of fine-tuning and accessibility for end users. He introduces the concept of using pre-trained models alongside advanced retrieval techniques, particularly focusing on how vector databases can enhance the performance of language models by effectively surfacing relevant context for specific queries.

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The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)
AI Agents and Data Integration with GPT and LLaMa with Jerry Liu - 628
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- I'm thinking that classical or deep ML solutions have a flaw in that they cannot be extended. For example, if one builds a model and wants to introduce a new feature, typically the model has to be retrained from scratch. So, I'm considering building a Knowledge Graph using LLMs. This knowledge graph would have to include time-dependent data (for example, it should be able to retrieve the "current" President of the USA and also previous presidents if asked). I'm thinking this Knowledge Graph could be used for Retrieval Augmented Generation (RAG) to help with business goals or maybe used with more clever User Interfaces (UIs). I'm not sure how to build or populate this KG and also have a rough idea of how to use it. Can you help me with suggesting particular paths for building and populating this Knowledge Graph?

Language Model Integration

In this clip

From this podcast

The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)

AI Agents and Data Integration with GPT and LLaMa with Jerry Liu - 628

Related Questions