4 ms·

I have the entirely unrefined notion, that, surely, lack of data is not what is keeping us from creating much, much better LLMs. I understand with how training

I have the entirely unrefined notion, that, surely, lack of data is not what is keeping us from creating much, much better LLMs. I understand with how training is done right now that more data makes things scale really well without having to come up with new concepts, but it seems completely obvious that better processing of already available knowledge is the way to make the next leaps. The idea is that, what is keeping me from having expert level knowledge in 50 different fields and using that knowledge to draw entirely new connections between all of them, in addition to understanding where things go wrong, is not lack of freely available expert level information. And yet, GPT4 barely reaches competency. It feels like computers should be able to get much more out of what is already available, specially when levering cross discipline knowledge to inform everything.

thfuran 3y ago
Yeah, no person has ever read anything like every textbook ever written, but that's pretty much table stakes for training sets. Clearly there's something missing aside from more virtual reading. (I suspect it has something to do with the half a billion years of pre-training baked into the human neural architecture and the few extra orders of magnitude in scale but who knows)
- all2 3y ago
  I'm sure I've read about specialized neural networks being created. The human brain has (apparently) a bunch of different kinds of neurons in it that specialize in processing different information. I'm not sure how that would work with our current architectures, though.
  - bpiche 3y ago
    Well Jeff Hawkins has been working on this for a while, in terms of biomimetic neural networks. They've done some great work but they don't have anything like modern language models in terms of abilities + performance. https://www.youtube.com/watch?v=cz-3WDdqbj0&list=PLX9JDz3uBpNCjNfq20KOCvsP6szY94r2e https://www.youtube.com/watch?v=cz-3WDdqbj0&list=PLX9JDz3uBp...
- HDThoreaun 3y ago
  People do analyze gigantic amounts of data constantly though. Sure it's not textbooks but the constant sensory data might be a big deal.
  - thfuran 3y ago
    I don't think annotated video datasets tend to be as large relative to human experience, but they'd still be decades or perhaps centuries of video.
- quickthrower2 3y ago
  In addition for animals/humans there are no software/hardware boundaries or any kind of static fixed deployment of either hardware or software.
cjohnson318 3y ago
I've found that Google's chat thing is wrong 90% of the time with coding questions. Yesterday I asked how to "crop" a geopandas dataframe to a specific area of interest, a lat/lng box, and it told me to use a dataframe function that's not even in the API. The "highest probability string" is useless if it's just dead wrong.
- thfuran 3y ago
  The remarkable thing about the current llms is that they're usable at all. For as much pushback as the idea seems to get, they really are a lot more like Markov chain generators than expert systems.
- robbrown451 3y ago
  I've had very different results. ChatGPT today is like a backhoe compared to a team of human with shovels. You still need a person who knows how to operate it, and their skills are different from those who dig with shovels. A bad backhoe operator is worse than any number of humans with shovels. Pretty soon it will be able to learn by running its own code and testing it by looking at its output, including with its "vision."
  - cjohnson318 3y ago
    > I've had very different results. That is very interesting. I can't think of a single time the Google built-in LLM has worked for me, let alone surprised and delighted me with a technical answer. I'm sure it's great at a lot of things, but it's not a replacement for SO yet.
    - robbrown451 3y ago
      Oh sorry you said Google. Yes I am speaking of ChatGPT, and I pay for GPT-4. It surprises and delights me on a regular basis. I have no doubt Google will catch up, but right now I think OpenAI is far out front.
      - 3y ago
famouswaffles 3y ago
>It feels like computers should be able to get much more out of what is already available I mean why ? It took millions of years of optimization for humanity to get to the competence level they're currently at. If you think you're "starting from scratch", you really aren't. Keep in mind LLMs can use significantly less data (but still a lot) when you're not trying to force feed the sum total of human knowledge. So should they be able to get more out of it ? or is this par the course for NNs?
- kelseyfrog 3y ago
  Because humans with less language data outperform LLMs with more language data. This either says we need better models, not more data. Or, the human ability to be multi-modal augments our ability to perform language tasks in which case, we need to pump LLMs with much more image and video input than we currently do.
  - famouswaffles 3y ago
    The point I'm making is that humans do not in fact have "less language data". We're heavily predisposed to learning languages. We don't start with random weights. GPT has no such predisposition.
    - kelseyfrog 3y ago
      Better init and architecture aren't what people think of when they think of "giving models more data" - they mean a larger training set.
      - famouswaffles 3y ago
        You're not making much sense here. The better init comes from training and little else. GPT needing lots of training data doesn't mean we need a better architecture. You would expect it to have a lot of training because humans have a lot of training too, spanning millions of years..
huytersd 3y ago
That sounds like pre optimization. In my opinion both things should happen in tandem. GPT4 is way, way above basic competency, I have no idea what you’re referring to.
- jstummbillig 3y ago
  By "competent", I mean pretty much what you would expect when you talk about a "competent programmer": A somewhat vague concept, yet fairly obvious when working with someone who whats up. If you would judge GPT4 to be a competent programmer your experience is wildly different from mine. (I am not sure why you felt the need to put a "basic" in there in reference to what I wrote, since that is not what I wrote).
  - huytersd 3y ago
    GPT4 is more than a competent programmer. It’s way, way above even a rockstar dev.
    - deleted 3y ago
      [deleted]
    - sgarland 3y ago
      I couldn’t get it to write a working B+tree implementation in Python. There was always some bug that would make it fail at some point. It’s good, don’t get me wrong, but if you go deep it’s usually incorrect somewhere.
      - afpx 3y ago
        It's pretty goood for me. It's saved me literally thosands of hours of work already. I ran a bunch of problems from Leetcode into it, and it got most of them right. Here's the b+tree implementation it gave me. I haven't checked if it's right. But, I was just curious what it'd come up with. https://chat.openai.com/share/a582aa43-cca8-426a-a4de-f45fdb34bf43 https://chat.openai.com/share/a582aa43-cca8-426a-a4de-f45fdb...
  - 3y ago
RandomLensman 3y ago
A lot of high quality information and data is not in the public (and not even for sale).
goodluckchuck 3y ago
I think you’re largely right, and that current GPT results may over-represent the model’s learning ability. A couple of speakers from Microsoft at the MPPC 2023 this week indicated that OpenAI’s models were not merely exposed to e.g. poetry, programming, etc. and learned those fields. Rather they were saying that the model is more of a composite of skills that were specifically trained, building on word identification, sentences, grammar, ultimately moving on to higher order skills. Perhaps this isn’t a secret (or perhaps I misunderstood), but it means the model’s ability to perform self-directed learning is much less than I previously though.

thfuran 3y ago

Yeah, no person has ever read anything like every textbook ever written, but that's pretty much table stakes for training sets. Clearly there's something missing aside from more virtual reading. (I suspect it has something to do with the half a billion years of pre-training baked into the human neural architecture and the few extra orders of magnitude in scale but who knows)

all2 3y ago
I'm sure I've read about specialized neural networks being created. The human brain has (apparently) a bunch of different kinds of neurons in it that specialize in processing different information. I'm not sure how that would work with our current architectures, though.
- bpiche 3y ago
  Well Jeff Hawkins has been working on this for a while, in terms of biomimetic neural networks. They've done some great work but they don't have anything like modern language models in terms of abilities + performance. https://www.youtube.com/watch?v=cz-3WDdqbj0&list=PLX9JDz3uBpNCjNfq20KOCvsP6szY94r2e https://www.youtube.com/watch?v=cz-3WDdqbj0&list=PLX9JDz3uBp...
HDThoreaun 3y ago
People do analyze gigantic amounts of data constantly though. Sure it's not textbooks but the constant sensory data might be a big deal.
- thfuran 3y ago
  I don't think annotated video datasets tend to be as large relative to human experience, but they'd still be decades or perhaps centuries of video.
quickthrower2 3y ago
In addition for animals/humans there are no software/hardware boundaries or any kind of static fixed deployment of either hardware or software.

cjohnson318 3y ago

I've found that Google's chat thing is wrong 90% of the time with coding questions. Yesterday I asked how to "crop" a geopandas dataframe to a specific area of interest, a lat/lng box, and it told me to use a dataframe function that's not even in the API. The "highest probability string" is useless if it's just dead wrong.

thfuran 3y ago
The remarkable thing about the current llms is that they're usable at all. For as much pushback as the idea seems to get, they really are a lot more like Markov chain generators than expert systems.
robbrown451 3y ago
I've had very different results. ChatGPT today is like a backhoe compared to a team of human with shovels. You still need a person who knows how to operate it, and their skills are different from those who dig with shovels. A bad backhoe operator is worse than any number of humans with shovels. Pretty soon it will be able to learn by running its own code and testing it by looking at its output, including with its "vision."
- cjohnson318 3y ago
  > I've had very different results. That is very interesting. I can't think of a single time the Google built-in LLM has worked for me, let alone surprised and delighted me with a technical answer. I'm sure it's great at a lot of things, but it's not a replacement for SO yet.
  - robbrown451 3y ago
    Oh sorry you said Google. Yes I am speaking of ChatGPT, and I pay for GPT-4. It surprises and delights me on a regular basis. I have no doubt Google will catch up, but right now I think OpenAI is far out front.
    - 3y ago

famouswaffles 3y ago

>It feels like computers should be able to get much more out of what is already available I mean why ? It took millions of years of optimization for humanity to get to the competence level they're currently at. If you think you're "starting from scratch", you really aren't. Keep in mind LLMs can use significantly less data (but still a lot) when you're not trying to force feed the sum total of human knowledge. So should they be able to get more out of it ? or is this par the course for NNs?

kelseyfrog 3y ago
Because humans with less language data outperform LLMs with more language data. This either says we need better models, not more data. Or, the human ability to be multi-modal augments our ability to perform language tasks in which case, we need to pump LLMs with much more image and video input than we currently do.
- famouswaffles 3y ago
  The point I'm making is that humans do not in fact have "less language data". We're heavily predisposed to learning languages. We don't start with random weights. GPT has no such predisposition.
  - kelseyfrog 3y ago
    Better init and architecture aren't what people think of when they think of "giving models more data" - they mean a larger training set.
    - famouswaffles 3y ago
      You're not making much sense here. The better init comes from training and little else. GPT needing lots of training data doesn't mean we need a better architecture. You would expect it to have a lot of training because humans have a lot of training too, spanning millions of years..

huytersd 3y ago

That sounds like pre optimization. In my opinion both things should happen in tandem. GPT4 is way, way above basic competency, I have no idea what you’re referring to.

jstummbillig 3y ago
By "competent", I mean pretty much what you would expect when you talk about a "competent programmer": A somewhat vague concept, yet fairly obvious when working with someone who whats up. If you would judge GPT4 to be a competent programmer your experience is wildly different from mine. (I am not sure why you felt the need to put a "basic" in there in reference to what I wrote, since that is not what I wrote).
- huytersd 3y ago
  GPT4 is more than a competent programmer. It’s way, way above even a rockstar dev.
  - deleted 3y ago
    [deleted]
  - sgarland 3y ago
    I couldn’t get it to write a working B+tree implementation in Python. There was always some bug that would make it fail at some point. It’s good, don’t get me wrong, but if you go deep it’s usually incorrect somewhere.
    - afpx 3y ago
      It's pretty goood for me. It's saved me literally thosands of hours of work already. I ran a bunch of problems from Leetcode into it, and it got most of them right. Here's the b+tree implementation it gave me. I haven't checked if it's right. But, I was just curious what it'd come up with. https://chat.openai.com/share/a582aa43-cca8-426a-a4de-f45fdb34bf43 https://chat.openai.com/share/a582aa43-cca8-426a-a4de-f45fdb...
- 3y ago

RandomLensman 3y ago

A lot of high quality information and data is not in the public (and not even for sale).

goodluckchuck 3y ago

I think you’re largely right, and that current GPT results may over-represent the model’s learning ability. A couple of speakers from Microsoft at the MPPC 2023 this week indicated that OpenAI’s models were not merely exposed to e.g. poetry, programming, etc. and learned those fields. Rather they were saying that the model is more of a composite of skills that were specifically trained, building on word identification, sentences, grammar, ultimately moving on to higher order skills. Perhaps this isn’t a secret (or perhaps I misunderstood), but it means the model’s ability to perform self-directed learning is much less than I previously though.