Learning from context is harder than we thought
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Learning from context is harder than we thought
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Re: Learning from context is harder than we thought
#2The key seems to be that you take the transcript of a model working within a problem domain that it’s not yet good at or where the context doesn’t match it’s original training and then you continually retrain it based on its efforts and guidance from a human or other expert. You end up with a specialty model in a given domain that keeps getting better at that domain, just like a human.
The hard part is likely when someone proves some “fact” which the models knows and has had reinforced by this training is no longer true. The model will take time to “come around” to understand this new situation. But this isn’t unlike the general populous. At scale humans accept new things slowly.
Re: Learning from context is harder than we thought
#3It's basically continual learning. This is beyond a hard problem it's currently an impossible one. I know of no system that solve CL even at small scale let alone large models.
Annoyingly, they have SOME inherent capability to do it. It's really easy to get sucked down this path due to that glimmer of hope but the longer you play with it the more annoying it becomes.
SSI seems to be focused on this problem directly so maybe they discover something?
Re: Learning from context is harder than we thought
#4wasn't in-context learning an emergent behavior a while ago (1-2 years)?
Re: Learning from context is harder than we thought
#5[deleted]
Re: Learning from context is harder than we thought
#6Because we don't experience reality through language but direct sensory perception. Language is arbitrary bird song and visual representations dragged forward from history, accepted definitions never uniformly distributed.
Testing based on contextual correctness makes no sense when there is no center to the universe. No "one true context to rule them all".
We learn from hands on sensory experiences. Our bodies store knowledge independent of the brain; often referred to as muscle memory.
Gabe Newell mentioned this years ago; our brain is only great at some things like language and vision processing but the rest of our body is involved in sensory information processing too: https://en.wikiquote.org/wiki/Gabe_Newell
The most potent evidence the brain is not the center of the universe we commonly think it to be is that patient with 90% of their skull filled with fluid while they carried out a typical first worlder life: https://www.sciencealert.com/a-man-who-lives-without-90-of-h...
States are banning a reading education framework that's been linked to lower literacy scores in younger generations; 3-cueing relies on establishing correctness via context assessment: https://www.edweek.org/teaching-learning/more-states-are-tak...
"Establishing context" is a euphemism for "arguing semantics".
Putting the brain at the root of of human intelligence is a relic of hierarchical and taxonomical models. There are no natural hierarchies.
Re: Learning from context is harder than we thought
#7Hmm.. I looked at the benchmark set.
I'm conflicted. I don't know that I would necessarily want a model to pass all of these. Here is the fundamental problem. They are putting the rules and foundational context in "user" messages.
Essentially I don't think you want to train the models on full compliance to the user messages, they are essentially "untrusted" content from a system/model perspective. Or at least it is not generally "fully authoritative".
This creates a tension with the safety, truthfulness training, etc.
Re: Learning from context is harder than we thought
#8Don't always trust everything you read in papers. Researchers are usually under incredible pressure to publish something, anything. Wait a few years and see if the paper survives the test of time. LLMs work reasonably fine for me in new domains.
Re: Learning from context is harder than we thought
#9Because we don't experience reality through language but direct sensory perception. Language is arbitrary bird song and visual representations dragged forward from history, accepted definitions never uniformly distributed. Testing based on contextual correctness makes no sense when there is no center to the universe. No "one true context to rule them all". We learn from hands on sensory experiences. Our bodies store…
”Because we don't experience reality through language but direct sensory perception”
That statement is patently false. We know that language influences our senses to a degree where we are unable to perceive things if our language doesn’t have a word for it, and will see different things as being equal if our language uses the same word for both.
There are examples of tribal humans not being able to perceive a green square among blue squares, because their language does not have a word for the green color.
Similarly, some use the same word for blue and white, and are unable to perceive them as different colors.
Re: Learning from context is harder than we thought
#10The key seems to be that you take the transcript of a model working within a problem domain that it’s not yet good at or where the context doesn’t match it’s original training and then you continually retrain it based on its efforts and guidance from a human or other expert. You end up with a specialty model in a given domain that keeps getting better at that domain, just like a human. The hard part is likely when so…
> But this isn’t unlike the general populous. At scale humans accept new things slowly.
right, the model works like humans at scale. Not like a human who reads the actual paper disproving the fact they thought was correct and is able to adapt. True not every human manages to do that, science advancing one death at a time, but some can.
But since the model is a statistical one, it works like humans at scale.