• DoPeopleLookHere@sh.itjust.works
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    14 hours ago

    Okay, here’s a non apple source since you want it.

    https://arxiv.org/abs/2402.12091

    5 Conclusion In this study, we investigate the capacity of LLMs, with parameters varying from 7B to 200B, to com- prehend logical rules. The observed performance disparity between smaller and larger models indi- cates that size alone does not guarantee a profound understanding of logical constructs. While larger models may show traces of semantic learning, their outputs often lack logical validity when faced with swapped logical predicates. Our findings suggest that while LLMs may improve their logical reason- ing performance through in-context learning and methodologies such as COT, these enhancements do not equate to a genuine understanding of logical operations and definitions, nor do they necessarily confer the capability for logical reasoning.

    • pinkapple@lemmy.ml
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      8 hours ago

      Another unpublished preprint that hasn’t published peer review? Funny how that somehow doesn’t matter when something seemingly supports your talking points. Too bad it doesn’t exactly mean what you want it to mean.

      “Logical operations and definitions” = Booleans and propositional logic formalisms. You don’t do that either because humans don’t think like that but I’m not surprised you’d avoid mentioning the context and go for the kinda over the top and easy to misunderstand conclusion.

      It’s really interesting how you get people constantly doubling down on specifically chatbots being useless citing random things from google but somehow Palantir finds great usage in their AIs for mass surveillance and policing. What’s the talking point there, that they’re too dumb to operate and that nobody should worry?