4 ms·
Looking towards the future, we will need to move away from "tokens are characters to print", We're kind of starting to consider this with "tool calls" but I bel
by firtoz 2y ago
Looking towards the future, we will need to move away from "tokens are characters to print", We're kind of starting to consider this with "tool calls" but I believe an architectural shift will become necessary.
We do have some kind of understanding of what kind of concept we want to emit next, e.g.
```
[The height:property name] of [Mount Bartle Frere:proper noun, describing an object to get a property out of], [in metres:attributes], is [?: retrieve value | (Mount Bartle Frere).("height", "metres")].
```
- giantrobot 2y agoUh oh, that sounds suspiciously like querying structured data. You can't hype SQL or worse, SPARQL, to investors!
- firtoz 2y agoWell, you can hype the results and then put it into a black box and call it a LLM anyway Which is pretty much what O1 etc are Update: it seems your recent submission[1] is pretty much that... interesting :D 1: https://github.com/caesarhq/textSQL https://github.com/caesarhq/textSQL
- Terr_ 2y agoA little bit RDF-y too. https://en.wikipedia.org/wiki/Resource_Description_Framework https://en.wikipedia.org/wiki/Resource_Description_Framework
- DoctorOetker 2y agoAre there any minimal / micro LLM's that are paired with a large RAG vector database, but still have performance on par with the huge LLM's? i.e. shifting parameters from weights to vector database, so that a smaller LLM can fit in RAM, and the vector database on disk. Possibly multiple calls. When you ask a human to switch context (changing topic) or to change activity (e.g. football to table tennis), they typically need some warm-up too, so it seems excessive to have all knowledge in high bandwidth RAM. It would seem basic mathematics, set theory etc should stay in RAM.