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by HarHarVeryFunny
655 days ago
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LLMs are here to stay until something better replaces them, and will be used for those things they are capable of. It shouldn't be surprising they are not great at reasoning, or everything one would hope for from an AGI, since they simply were not built for that. If you look at the development history, the transformer was a successor to LSTM-based seq-2-seq models using Bahdanau attention, whose main goal was to more efficiently utilize parallel hardware by supporting parallel processing. Of course a good language model (word predictor) will look as if it's reasoning because it is trying to model the data it was trained on - a human reasoner. As humans we routinely think for seconds/minutes or even hours before speaking or acting, while an LLM only has that fixed N steps (layers) of computation. I don't know why you claim this difference (among others) should make no difference, but it clearly does, with out-of-training-set reasoning weakness being a notable limitation that people such as Demis Hassabis have recently conceded. |
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>As humans we routinely think for seconds/minutes or even hours before speaking or acting
No human is iterating on a base thought for hours uninterrupted lol so this is just moot
>with out-of-training-set reasoning weakness being a notable limitation that people such as Demis Hassabis have recently conceded.
Humans reason weaker out of training. LLMs are simply currently worse