LLM Challenges Ahead

Alireza discusses the current limitations of large language models, particularly in recall and grounding issues, while highlighting the rapid advancements in open-source frameworks that aim to address these challenges. He shares a practical example of automating responses for a vacation rental business, showcasing the potential of LLMs when paired with effective prompt engineering. The conversation emphasizes the ongoing evolution of models and their applications in real-world scenarios.

In this clip
From this podcast
The Startup Podcast
Why does AI Alignment = Slavery?
Related Questions
- 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?

LLM Challenges Ahead

In this clip

From this podcast

The Startup Podcast

Why does AI Alignment = Slavery?

Related Questions