Comment by euroderf

10 months ago

When I first learned about LLMs, what came to mind is some sort of "meeting of the minds" with Cyc. 'Twas not to be, apparently.

5 comments

euroderf

imglorp 10 months ago

I view Cyc's role there as a RAG for common sense reasoning. It might prevent models from advising glue on pizza.

    (is-a 'pizza 'food)
    (not (is-a 'glue 'food))
    (for-all i ingredients
      (assert-is-a i 'food))

DonHopkins 10 months ago

You must not read ingredients on products or eat library paste. ;)
Also: MEAT GLUE (transglutanimase)!
https://en.wikipedia.org/wiki/Transglutaminase
https://en.wikipedia.org/wiki/Molecular_gastronomy
https://lyndeymilan.blogspot.com/2010/01/question-what-is-oc...
https://www.youtube.com/watch?v=oA8sNJjlQzU
https://www.youtube.com/watch?v=XAVO6hzNTns
jes5199 10 months ago
sure but the bigger models don’t make these trivial mistakes, and I’m not sure if translating the LLM english sentences into LISP and trying to check them is going to be more accurate than just training the models better
- yellowapple 10 months ago
  
  The bigger models avoid those mistakes by being, well, bigger. Offloading to a structured knowledgebase would achieve the same without the model needing to be bigger. Indeed, the model could be a lot smaller (and a lot less resource-intensive) if it only needed to worry about converting $LANGUAGE queries to Lisp queries and converting Lisp results back into $LANGUAGE results (where $LANGUAGE is the user's natural language, whatever that might be), rather than having to store some approximation of that knowledgebase within itself on top of understanding $LANGUAGE and understanding whatever ad-hoc query/result language it's unconsciously invented for itself.
- pfdietz 10 months ago
  
  Beyond just checking for mistakes, it would be interesting to see if Cyc has concepts that the LLMs don't or vice versa. Can we determine this by examining the models' internals?