alt Hacker NewsNot learning from new input may be a feature. Back in 2016 Microsoft launched one that did, and after one day of talking on Twitter it sounded like 4chan.[1] If all input is believed equally, there's a problem.
Today's locked-down pre-trained models at least have some consistency.
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I think models should be “forked”, and learn from subsets of input and themselves. Furthermore, individuals (or at least small groups) should have their own LLMs.
Sameness is bad for an LLM like it’s bad for a culture or species. Susceptible to the same tricks / memetic viruses / physical viruses, slow degradation (model collapse) and no improvement. I think we should experiment with different models, then take output from the best to train new ones, then repeat, like natural selection.
And sameness is mediocre. LLMs are boring, and in most tasks only almost as good as humans. Giving them the ability to learn may enable them to be “creative” and perform more tasks beyond humans.
That one 4chan troll delayed the launch of LLM like stuff by Google for about 6 years. At least that's what I attribute it to.
Yes I like that /clear starts me at zero again and that feels nice but I am scared that'll go away.
Like when Google wasn't personalized so rank 3 for me is rank 3 for you. I like that predictability.
Obviously ignoring temperature but that is kinda ok with me.
Yeah deep learning treats any training data as the absolute god given ground truth and will completely restructure the model to fit the dumbest shit you feed it.
The first LLMs were utter crap because of that, but once you have just one that's good enough it can be used for dataset filtering and everything gets exponentially better once the data is self consistent enough for there to be non-contradictory patterns to learn that don't ruin the gradient.
Incredible to accomplish that in a day - it took the rest of the world another decade to make Twitter sound like 4chan, but thanks to Elon we got there in the end.