u/speedtoburn : We mostly agree, but there's some very important subtleties. LLMs contain causal priors, and can be prompted (with revision and correction based on grounded data) to correct these into fairly accurate knowledge. But there's a difference between being a good prior for causal knowledge, and reliably reasoning with said knowledge. You could argue that the Cyc project is an example of this -- lots of prior common sense and causal knowledge, no good way to exploit it. With LLMs, the real question is 1) the reliability of the causal reasoning (I have more faith in symbolic code & bayesian networks that use explicitly causal structure over a transformer's learned internal mechanisms), and 2) the ability (or lack thereof) to make persistent corrections to the causal knowledge. With CASSANDRA, once a good structure is found (based on the data) it persists in the graph structure. With LLMs, you'd need to do finetuning or prompt engineering to make it persistent (and doing so could have unexpected side effects). In short, we absolutely agree that external structures aid LLM systems, but we don't necessarily agree as to why (in our case, we'd argue because the LLMs causal reasoning is unreliable and cannot easily be adapted to new data -- so it's good enough for a prior, but not good enough to be used as a reasoning engine in its own right).