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.