Post #3062635
2025-10-06 20:15 UTC
Replies (2)
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@nicolaromano@qoto.org 2025-10-06 21:11
@PalmAndNeedle@norden.social @Kiloku@burnthis.town @eb@social.coop Because code is a series of words, which is what LLMs generate. They're not designed to do calculations. Indeed, sometimes when asked to calculate something these systems generate code that when executed gives the (mathematically correct, of course) answer. The reality is, very often LLMs generate good, working code. The issue is not that. There are big ethical and environmental issues. Also, because the code works often, but not always, you need to double check it at all times, effectively taking longer then writing it by yourself (eg https://metr.org/blog/2025-07-10-early-2025-ai-experienced-os-dev-study/).
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@onterof@sueden.social 2025-10-06 21:32
@PalmAndNeedle@norden.social @nicolaromano@qoto.org @Kiloku@burnthis.town @eb@social.coop A partial distinction is helpful: From an engineering viewpoint current models are very good at predicting the next token, e.g. generating plausible text etc. But task like math Wege not considered in the training of these models. So it's expected they are not very good with it. The classical definition of model has "pragmatic feature" (use case) as a key part. There are models that are not to bad even with formal mathemtatics.