No, your opinion is wrong because the reason some models don't seem to have some "strong opinion" on anything is not related to predicting words based on how similar they are to other sentences in the training data. It's most likely related to how the model was trained with reinforcement learning, and most specifically, to recent efforts by OpenAI to reduce hallucination rates by penalizing guessing under uncertainty[1].

[1] https://cdn.openai.com/pdf/d04913be-3f6f-4d2b-b283-ff432ef4a...

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hansmayer 2 hours ago | parent [-]

Well, you do understand the "penalising" or as the ML scientific community likes to call it - "adjusting the weights downwards" - is part of setting up the evaluation functions, for gasp - calculating the next most likely tokens, or to be more precise, tokens with the highest possible probability? You are effectively proving my point, perhaps in a bit hand-wavy fashion, that nevertheless still can be translated into the technical language.

	▲	andy12_ an hour ago \| parent [-]
		You do understand that the mechanism through which an auto-regressive transformer works (predicting one token at a time) is completely unrelated to how a model with that architecture behaves or how it's trained, right? You can have both: - An LLM that works through completely different mechanisms, like predicting masked words, predicting the previous word, or predicting several words at a time. - A normal traditional program, like a calculator, encoded as an autoregressive transformer that calculates its output one word at a time (compiled neural networks) [1][2] So saying "it predicts the next word" is a nothing-burger. That a program calculates its output one token at a time tells you nothing about its behavior. [1] https://arxiv.org/pdf/2106.06981 [2] https://wengsyx.github.io/NC/static/paper_iclr.pdf