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Comment by Workaccount2 | Hacker News Reader

What gets me, and the author talks about it in the post, is that people will readily attribute correct answers to "its in the training set" but nobody says anything about incorrect answers that are in the training set. LLMs get stuff in the training set wrong all the time, but nobody uses it as evidence that it probably can't lean too hard on it's memorization for complex questions it does get right.

It puts LLMs in an impossible position; if they are right, they memorized it, if they are wrong, they cannot reason.

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> It puts LLMs in an impossible position; if they are right, they memorized it, if they are wrong, they cannot reason.

Both of those can be true at the same time though. They memorize a lot of things, but its fuzzy and when they remember wrong they cannot fix it via reasoning.

It's more than fuzzy, they are packing exabytes, perhaps zetabytes of training data into a few terabytes. Without any reasoning ability it must be divine intervention that they ever get anything right...

It is divine intervention if you believe human minds are the product of a divine creator. Most of the attribution of miraculous reasoning ability on the part of LLMs I would attribute to pareidolia on the part of their human evaluators. I don’t think we’re much closer at all to having an AI which can replace an average minimum wage full-time worker, who will work largely unsupervised but ask their manager for help when needed, without screwing anything up.

We have LLMs that can produce copious text but cannot stop themselves from attempting to solve a problem they have no idea how to solve and making a mess of things as a result. This puts an LLM on the level of an overly enthusiastic toddler at best.

LLMs are trained with hundreds of terabytes of data to a few petabyte at most. You are off by 3 to 6 orders of magnitude in your estimate of training data. They aren't literally trained on "all the data of the internet". That would be a divergent nightmare. Catastrophic forgetting is still a problem with neural networks and ML algorithms in general. Humans are probably trained on less than half an exabyte of data given the ~1Gbps of sensory data we receive in a lifetime. That's still ~20 petabytes of data by age 5. A 400B parameter LLM with 100 examples per parameter would equal about 640 TB (F16 parameters) of training data. That's the order of magnitude of current models.

Do you hypothese that they see more wrong examples then right? Why is there concern about model collapse if they are reasoning and can sort it out, why does the data even need to be scrubbed before training?

How many r's really are in Strawberry?

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