6 ms·

Do LLMs use neural nets? If so, what makes up the "neuron"? i.e. Is there a code structure that underlies the neuron, or is it "just" fancy math?

theonlybutlet 3y ago
Yes to both, the "neuron" would basically be a weighted parameter. A parameter is an expression, it's a mathematical representation of a token and it's probabilistic weighting (theyre translated from input or to output token lists entering and exiting the model). Usually tokens are pre-set small groups of character combinations like "if " or "cha" that make up a word/sentence. The recorded path your value takes down the chain of probabilities would be the "neural pathway" within the wider "neural network". Someone please correct me if I'm wrong or my terminology is wrong.
- pmayrgundter 3y ago
  This is all true in a neutral net, but Transformers aren't Neural Nets in the traditional sense. I was under that impression originally, but there's not a back propagation or Hebbian learning here, which were the key bits of biomimicry that earned classic NNs their name. Transformers do have coefficients that are fit, but that's more broad.. could be used for any sort of regression or optimization, and not necessarily indicative of biological analogs. So I think the terms "learned model" of "weights" are malapropisms for Transformers, carried over from deep nets because of structural similarities, like many layers, and the development workflow. The functional units in Transformer's layers have lost their orginal biological inspiration and functional analog. The core function in Transformers is more like autoencoding/decoding (concepts from info theory) and model/grammar-free translation, with a unique attention based optimization. Transformers were developed for translation. The magic is smth like "attending" to important parts of the translation inputs&outputs as tokens are generated, maybe as a kind of deviation on pure autoencoding, due to the bias from the .. learned model :) See I can't even escape it. Attention as a powerful systemic optimization is the actual closer bit of neuro/bio-insporation here.. but more from Cog Psych than micro/neuro anatomy. Btw, not only is attention a key insight for Transformers, but it's an interesting biographical note that the lead inventor of it, Jakob Uzkereit, went on to work on a bio-AI startup after Google.
  - theonlybutlet 3y ago
    Thanks for your reply, you raise a very good point, transformer models are a lot more complex. I'd argue conceptually they're the same, just the data and process is more abstracted. Autoencoded data implies using efficient representations, basically semantically abstracted data and opting for measures like back propagation through time.
    - pmayrgundter 3y ago
      So like in my sister reply, I don't see the Backprop, but maybe I'm missing it. This article does use the word, but in a generic way "For example, when doing the backpropagation (the technique through which the models learn), the gradients can become too large" But I think this is more of a borrowing and it's not used again in description and may just be a misconception. There's no use of the Backprop term in the original paper nor any stage of learning where output errors are run thru the whole network in a deep regression. What I do see in Transformers is localized uses of gradient descent, and Backprop in NNs also uses GD...but that seems the extent of it. Is there a deep regression? Maybe I'm missing it
      - theonlybutlet 3y ago
        Yes, if the below perhaps helps. Over my head but... https://courses.grainger.illinois.edu/ece448/sp2023/slides/lec24.pdf https://courses.grainger.illinois.edu/ece448/sp2023/slides/l... From another source: Backpropagation Through Time (BPTT) is an adaptation of backpropagation used for training recurrent neural networks (RNNs), which are designed to process sequences of data and have internal memory. Because the output at a given time step might depend on inputs from previous time steps, the forward pass involves unfolding the RNN through time, which essentially converts it into a deep feedforward neural network with shared weights across the time steps. The error for each time step is computed, and then BPTT is used to calculate the gradients across the entire unfolded sequence, propagating the error not just backward through the layers but also backward through the time steps. Updates are then made to the network weights in a way that should minimize errors for all time steps. This is computationally more involved than standard backpropagation and has its own challenges such as exploding or vanishing gradients"
dartos 3y ago
Transformers can be considered a kind of neural network. It’s mainly fancy math. With tools like PyTorch or tensorflow, you use python to describe a graph of computations which gets compiled down into optimized instructions. There are some examples of people making transformers and other NN architectures in about 100 lines of code. I’d google for those to see what these things look like in code. The training loop, data, and resulting weights are where the magic is. The code is disappointingly simple.
- bloopernova 3y ago
  > The code is disappointingly simple. I absolutely adore this sentence, it made me laugh to imagine coders or other folks looking at the code and thinking "That's it?!? But that's simple!" Although it feels a little similar to some of the basic reactions that go to make up DNA: start with simple units that work together to form something much more complex. (apologies for poor metaphors, I'm still trying to grasp some of the concepts involved with this)
  - edgyquant 3y ago
    Yes neural networks, and even the math required to build them, are very simple calc 1 stuff generally. It’s more coming up with these models that takes powerful intuition
  - dartos 3y ago
    I spent a solid month very confused after reading up on how to implement some basic neural networks. I was sure I missed something, so I didn’t even try to implement it since I was so sure I missed the complicated bit. But no, all the complexity is in the mathematical implications
osanseviero 3y ago
Just math, and not even that fancy. Let's say you want to predict if you'll pass an exam based on how many hours you studied (x1) and how many exercises you did (x2). A neuron will learn a weight for each variable (w1 and w2). If the model learns w1=0.5 and w2=1, the model will provide more importance to the # of exercises. So if you study for 10 hours and only do 2 exercises, the model will do x1w1 + x2w2=10x0.5 + 2x1 = 7. The neuron then outputs that. This is a bit (but not much) simplified - we also have a bias term and an activation to process the output. Congrats! We built our first neuron together! Have thousands of these neurons in connected layers, and you suddenly have a deep neural network. Have billions or trillions of them, you have an LLM :)
abrichr 3y ago
The “neuron” in a neural network is just a non linear function of the weighted sum of the inputs (plus a bias term). See the “definition” section in https://en.wikipedia.org/wiki/Perceptron https://en.wikipedia.org/wiki/Perceptron .

theonlybutlet 3y ago

Yes to both, the "neuron" would basically be a weighted parameter. A parameter is an expression, it's a mathematical representation of a token and it's probabilistic weighting (theyre translated from input or to output token lists entering and exiting the model). Usually tokens are pre-set small groups of character combinations like "if " or "cha" that make up a word/sentence. The recorded path your value takes down the chain of probabilities would be the "neural pathway" within the wider "neural network". Someone please correct me if I'm wrong or my terminology is wrong.

pmayrgundter 3y ago
This is all true in a neutral net, but Transformers aren't Neural Nets in the traditional sense. I was under that impression originally, but there's not a back propagation or Hebbian learning here, which were the key bits of biomimicry that earned classic NNs their name. Transformers do have coefficients that are fit, but that's more broad.. could be used for any sort of regression or optimization, and not necessarily indicative of biological analogs. So I think the terms "learned model" of "weights" are malapropisms for Transformers, carried over from deep nets because of structural similarities, like many layers, and the development workflow. The functional units in Transformer's layers have lost their orginal biological inspiration and functional analog. The core function in Transformers is more like autoencoding/decoding (concepts from info theory) and model/grammar-free translation, with a unique attention based optimization. Transformers were developed for translation. The magic is smth like "attending" to important parts of the translation inputs&outputs as tokens are generated, maybe as a kind of deviation on pure autoencoding, due to the bias from the .. learned model :) See I can't even escape it. Attention as a powerful systemic optimization is the actual closer bit of neuro/bio-insporation here.. but more from Cog Psych than micro/neuro anatomy. Btw, not only is attention a key insight for Transformers, but it's an interesting biographical note that the lead inventor of it, Jakob Uzkereit, went on to work on a bio-AI startup after Google.
- theonlybutlet 3y ago
  Thanks for your reply, you raise a very good point, transformer models are a lot more complex. I'd argue conceptually they're the same, just the data and process is more abstracted. Autoencoded data implies using efficient representations, basically semantically abstracted data and opting for measures like back propagation through time.
  - pmayrgundter 3y ago
    So like in my sister reply, I don't see the Backprop, but maybe I'm missing it. This article does use the word, but in a generic way "For example, when doing the backpropagation (the technique through which the models learn), the gradients can become too large" But I think this is more of a borrowing and it's not used again in description and may just be a misconception. There's no use of the Backprop term in the original paper nor any stage of learning where output errors are run thru the whole network in a deep regression. What I do see in Transformers is localized uses of gradient descent, and Backprop in NNs also uses GD...but that seems the extent of it. Is there a deep regression? Maybe I'm missing it
    - theonlybutlet 3y ago
      Yes, if the below perhaps helps. Over my head but... https://courses.grainger.illinois.edu/ece448/sp2023/slides/lec24.pdf https://courses.grainger.illinois.edu/ece448/sp2023/slides/l... From another source: Backpropagation Through Time (BPTT) is an adaptation of backpropagation used for training recurrent neural networks (RNNs), which are designed to process sequences of data and have internal memory. Because the output at a given time step might depend on inputs from previous time steps, the forward pass involves unfolding the RNN through time, which essentially converts it into a deep feedforward neural network with shared weights across the time steps. The error for each time step is computed, and then BPTT is used to calculate the gradients across the entire unfolded sequence, propagating the error not just backward through the layers but also backward through the time steps. Updates are then made to the network weights in a way that should minimize errors for all time steps. This is computationally more involved than standard backpropagation and has its own challenges such as exploding or vanishing gradients"

dartos 3y ago

Transformers can be considered a kind of neural network. It’s mainly fancy math. With tools like PyTorch or tensorflow, you use python to describe a graph of computations which gets compiled down into optimized instructions. There are some examples of people making transformers and other NN architectures in about 100 lines of code. I’d google for those to see what these things look like in code. The training loop, data, and resulting weights are where the magic is. The code is disappointingly simple.

bloopernova 3y ago
> The code is disappointingly simple. I absolutely adore this sentence, it made me laugh to imagine coders or other folks looking at the code and thinking "That's it?!? But that's simple!" Although it feels a little similar to some of the basic reactions that go to make up DNA: start with simple units that work together to form something much more complex. (apologies for poor metaphors, I'm still trying to grasp some of the concepts involved with this)
- edgyquant 3y ago
  Yes neural networks, and even the math required to build them, are very simple calc 1 stuff generally. It’s more coming up with these models that takes powerful intuition
- dartos 3y ago
  I spent a solid month very confused after reading up on how to implement some basic neural networks. I was sure I missed something, so I didn’t even try to implement it since I was so sure I missed the complicated bit. But no, all the complexity is in the mathematical implications

osanseviero 3y ago

Just math, and not even that fancy. Let's say you want to predict if you'll pass an exam based on how many hours you studied (x1) and how many exercises you did (x2). A neuron will learn a weight for each variable (w1 and w2). If the model learns w1=0.5 and w2=1, the model will provide more importance to the # of exercises. So if you study for 10 hours and only do 2 exercises, the model will do x1w1 + x2w2=10x0.5 + 2x1 = 7. The neuron then outputs that. This is a bit (but not much) simplified - we also have a bias term and an activation to process the output. Congrats! We built our first neuron together! Have thousands of these neurons in connected layers, and you suddenly have a deep neural network. Have billions or trillions of them, you have an LLM :)

abrichr 3y ago

The “neuron” in a neural network is just a non linear function of the weighted sum of the inputs (plus a bias term). See the “definition” section in https://en.wikipedia.org/wiki/Perceptron https://en.wikipedia.org/wiki/Perceptron .