I think that the natural language understanding capability of current LLMs is undervalued.
To understand what the user meant before LLM's we had to train several NLP+ML models in order to get something going but in my experience we'll never get close to what LLM's do now.
I remember the first time I tried ChatGPT and I was surprised by how well it understood every input.
It's parsing. It's tokenizing. But it's a stretch to call it understanding. It creates a pattern that it can use to compose a response. Ensuring the response is factual is not fundamental to LLM algorithms.
In other words, it's not thinking. The fact that it can simulate a conversation between thinking humans without thinking is remarkable. It should tell us something about the facility for language. But it's not understanding or thinking.
To understand what the user meant before LLM's we had to train several NLP+ML models in order to get something going but in my experience we'll never get close to what LLM's do now.
I remember the first time I tried ChatGPT and I was surprised by how well it understood every input.