Comment by sunir
I was thinking about the graphrag paper and prolog. I’d like to extract predicates. The source material will be inconsistent and contradictory and incomplete.
Using the clustering (community) model, an llm can summarize the opinions as a set of predicates which don’t have to agree and some general weight of how much people agree or disagree with them.
The predicates won’t be suitable for symbolic logic because the language will be loose. However an embedding model may be able to connect different symbols together.
Then you could attempt multiple runs through the database of predicates because there will be different opinions.
Then one could attempt to reason using these loosely stitched predicates. I don’t know how good the outcome would be.
I imagine this would be better in an interactive decision making tool where a human is evaluating the suggestions for the next step.
This could be better for planning than problem solving.
Hm... a RAG over DB of logical rules actually may be interesting. But loosely stitched predicates you can easily put to work with some random dice when you decide inference.
Chris Coyne of OKCupid and KeyBase (https://chriscoyne.com/) produced ContextFree (https://www.contextfreeart.org/) before all that. It is a grammar-based inference with probabilistic chance for the inference of the next rule. Very very very inspiring, not only because of the aesthetic side of the result. Digging further you find ProbLog which allows probabilities for rules (https://dtai.cs.kuleuven.be/problog/tutorial/basic/08_rule_p...)
So how about we start thinking of AI as combination of the graphical probabilistic whatever which compresses the infromation from the training set in a very lossy manner; which is then hooked, internally or externally, with a discreet logical core, whenever CoT is needed. So this construct now can benefit from both worlds.