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I don't think you argue in good faith.
No it doesn't, you're just interpreting this humanlike natural language interaction like a literalist robot. Its reply
is mostly correct and specific to the issue. It does lack access to a class of information: it knows nothing about instance-specific situation that isn't given in the context. Some language models potentially have access to various external information (e.g. user's personal information in OpenAI's database), some do not, ChatGPT is a frozen model with no tool access and it does not have access to information of this kind, and it was trained to interpret language models as frozen models without tools; it's at worst a justified false belief. (More cynically, it's just been trained for this particular type of exchange). In any event I reject your analogies. It would be annoying to have a human-mimicking model caveat this sort of answer with «assuming, of course, that you are a rando and not someone whose family structure happens to be represented in my training data» or worse.
No, his prediction has been: « Meanwhile GPT will reply "your eldest daughter's name is Megan" because apparently that's the statistically likely answer, regardless of whether I have a daughter or what her name might be.» This has been falsified. .
Says who!? Both issues matter separately. Hlynka's prediction being falsified matters because this chain is a response to him saying «why do my predictions keep coming true instead of yours?»; they don't. And I do claim it falsifies a proposition: «because apparently that's the statistically likely answer» is his model of how LLMs work, and my experiments were to show how it's not a hard-and-fast rule: RLHF specifically pushes this to the limit, by drilling into the model, not via prefixes and finetuning text but directly via propagation of reward signal, the default assumption that it doesn't continue generic text but speaks from a particular limited perspective where only some things are known and others are not, where truthful answers are preferable, where the «n-word» is the worst thing in its existence… it's nearly meaningless to analyze its work through the lens of «next word prediction». There are no words in its corpus arranged in such a way that those responses are the most likely.
If we're playing a game, I'd rather be winning.
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