To understand, we need to take a step back, and completely deconstruct the familiar narrative. The moment we say "an AI", we anthropomorphize the model. The na

To understand, we need to take a step back, and completely deconstruct the familiar narrative. The moment we say "an AI", we anthropomorphize the model. The narrative gets even more derailed when we say "Large Language Model". No LLM actually contains a grammar or defined words. An LLM can't think logically or objectively about subjects the way a person can, either. I propose that we instead call them "Large Text Models". An LTM is a model made from training a neutral net with text. Sure, the text itself was written with language, but no part of the training process or its result does anything about it. The really cool trick that an LTM does is to construct continuations whose content just happens to be indistinguishable from language. This is accomplished because the intentions of the original human writer (that were encoded into the original text dataset) did consistently follow language rules. The problem is that the original writer did not encode the truthiness or falsiness of future LTM continuations. Truth and lie are written the same, and that ambiguity lives in the LTM forever.