I suspect we are getting into an issue of degrees, potentially due to differences in how you and I have been applying LLMs. For example you said that in the av

I suspect we are getting into an issue of degrees, potentially due to differences in how you and I have been applying LLMs. For example you said that in the average case they actually represent concepts by the complex interplay of adjacent concepts - I would agree. ChatGPT can pass the bar, it can pass medical exams etc. I would also point out that the work in that sentence is being done by the term “average case”. Let’s assume our experiences diverge at this point. At the start of the year, I started tinkering, then actively trying to push LLMs to failure, in order to understand the limits of what could be achieved. After creating several tools/experiments you end up having to deal with Hallucinations, and this is where my stance likely diverged from yours. Two different studies showed generated content was only ~50% and ~40% supported by provided citations. One out of 4 of my summarization tests was spectacularly fabricated. I had bad performance on even classification tasks - and OpenAI engineers described this same failure at a conference. I am a recovering non-coder, so you dont have to take my word for it. At work, I need processes that are more than ~97.x% accurate, otherwise they are poor replacements for the human in the loop ones already in place. Average case performance suggested the ability to actively plan, to actively assess situations. However hallucinations overrode those capabilities. LLMs will actively imagine functions, teams, or processes that dont exist. Eventually, it became clear that LLM hallucination is far too anthropomoprhized a word. LLMs are always “hallucinating” - it’s only humans who have an issue with the output. If I have understand you correctly, semantic inaccuracy to you is simply a failure of not having enough related concepts. I wish I could remember the exact examples that made me realize there is no world view at play at all, I could simply share those. Instead, can you describe how you are getting acceptable performance from your LLMs? Maybe experience and use cases will be enough to bridge the gap.