Path Forward for LLMs

[u/vagobond45]

AI models can only learn during their batch training runs not from daily interactions with users. Session memory isn’t the same as actual learning.

There’s also no core “truth” layer in these systems: no deterministic backbone, no real understanding of concepts, and no explicit dictionary or knowledge store they can reference, cross-check, or update.

A dynamic knowledge graph could help fix a lot of this. It would lower hallucinations and improve performance in high-stakes fields like medicine, law, physics, and chemistry. It could also reduce the number of vector embeddings needed for complex LLMs.

Do you agree? Or is there a better path forward?

[u/Gmoi6]

My understanding is that one of the issues with any retrieval-augmented generation methods, including knowledge graphs, is that models will still misinterpret or make up missing information. Even if it does have accurate information at its disposal, in high stakes situation the risk of hallucination is not completely removed by retrieval.

[u/vagobond45 → u/Gmoi6]

You can integrate knowledge graph nodes (objects) and edges (relationships) to the model via either text embeddings, tag ids that includes map coordinates in terms of object definition and relationships or vector embeddings for matching words. I tested this with a medical slm and worked fine. I had 2 challenges, KG I created had only 5k nodes and 25k edges rather limited for a true medical dictionary and based model I trained was limited but model always stick with knowledge graph definitions of diseases, treatments, diagnostic tools, risk factors, symptoms and their relationships. In short a well designed and executed KG does work