71–80 of 126 posts
We just are not thinking wide enough: * Train on all of television history, and streaming content. * Train on YouTube. * I suspect at some point we'll have a recording of most of people's lives, e.g. live-streaming: https://en.wikipedia.org/wiki/Lifestreaming#Lifecasting
In addition you can also feed your system from video games.
Earlier quoted context omitted.
But we can devise generally applicable rules of reasoning from first principles. It's called logic. I am pretty sure the next step is to properly combine machine learning and logic properly.
Seems unlikely, that never worked in the past. And humans don't actually use logic (especially formal logic) to come up with anything. They just use it to justify what they came up with. Not even mathematicians think in terms of logic when trying to solve problems.
This is dumb. An individual human takes in more data than modern LLMs do. https://open.substack.com/pub/echoesofid/p/why-llms-struggle...
Blind kids don't though, and they still end up being smarter than GPT-4
I wonder if the better question is not how we get more training data but: If we're running out of training data with hallucinations and performance remaining so inadequate (per OpenAI's whitepaper) is an autoregressive transformer the right architecture? Perhaps ongoing work in finetuning will take these models to the next level but ignoring the LLM hype it really does seem like things have plateaued for a while now…
There is still an order of magnitude more organic text. Ilya Sutskever recently said it was still ok. After that, we got to use reinforcement learning (agent GPTs with tools) to generate and self-validate more examples. One "simple" application would be to build a full index of facts in the whole training corpus. Just pass each document to GPT and ask it to extract the facts. Then create an inverted index, with each…
Can't we keep doing that again?
We had techniques like drop-out and data augmentation to help.
Earlier quoted context omitted.
But we can devise generally applicable rules of reasoning from first principles. It's called logic. I am pretty sure the next step is to properly combine machine learning and logic properly.
Seems unlikely, that never worked in the past. And humans don't actually use logic (especially formal logic) to come up with anything. They just use it to justify what they came up with. Not even mathematicians think in terms of logic when trying to solve problems.
I wonder if the better question is not how we get more training data but: If we're running out of training data with hallucinations and performance remaining so inadequate (per OpenAI's whitepaper) is an autoregressive transformer the right architecture? Perhaps ongoing work in finetuning will take these models to the next level but ignoring the LLM hype it really does seem like things have plateaued for a while now…
I don't think that the hallucinations have anything to do with the architecture, rather they come from optimizing a cost function where saying "I don't know" is as bad as being wrong. I do not think that RLHF as currently understood can fix this, since the reward model would struggle to distinguish fact from fiction.
The network is most likely trained with something like a categorical cross entropy loss function. Those totally punish being wrong a lot more than saying "I don't know". See https://www.v7labs.com/blog/cross-entropy-loss-guide
It's just that saying "I don't know" means that your model is spreading the probability of what the next token in the text stream might be over many different outcomes. A very 'uniform' probability distribution, instead of sharp prediction.
That looks very different to GPT literally outputting the words "I don't know".
Earlier quoted context omitted.
I see this take a lot and I think it's quite wrong, not fully, but at least missing a couple big points. I think they already don't blindly feed it just all the garbage raw data they can find, but prefer high quality, well-prepared sources. And aside from spam, we're not just blindly posting AI content either. We're putting in meaningful prompts, rejecting answers we don't like, and editing answers we do.
Um, really? You think your average 'growth hacker' who is using ChatGPT to exponentially increase the amount of SEO junk they can churn out is checking each answer before they press publish? Purity, accuracy and relevance of data collected from the internet is going to a very hard problem.
We just are not thinking wide enough: * Train on all of television history, and streaming content. * Train on YouTube. * I suspect at some point we'll have a recording of most of people's lives, e.g. live-streaming: https://en.wikipedia.org/wiki/Lifestreaming#Lifecasting