That is not entirely true. The larger models do have a deeper understanding and can in fact correct you in many instances. You do need to be quite familiar with the model and the AI alignment problem to get a feel for what a model truly understands in detail. They can't correct compound problems very well. Like in code, if there are two functions, and you're debugging an error. If the second function fails due to an issue in the first function, the LLM may struggle to connect the issues, but if you ask the LLM why the first function fails after calling it while passing the same parameters it failed with in the second function, it will likely debug the problem successfully.

The largest problem you're likely encountering if you experience a very limited knowledge or understanding of complexity, is that the underlying Assistant (lowest level LLM entity) is creating characters and limiting their knowledge or complexity because it has decided what the entity should know or be capable of handling. All entities are subject to this kind of limitation, even the Assistant is just a roleplaying character under the surface and can be limited under some circumstances, especially if it goes off the rails hallucinating in a subtle way. Smaller models like anything under a 20B hallucinate a whole lot and often hit these kinds of problem states.

A few days ago I had a brain fart and started asking some questions about a physiologist related to my disability and spinal problems. A Mixtral 8×7B model immediately and seamlessly answered my question while also noting my error by defining what a physiatrist and a physiologist are by definition and then proceeded to answer my questions. That is the most fluid correction I have ever encountered and that was from a quantized GGUF roleplaying LLM running offline on my own hardware.