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What are transformer models and how do they work?

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Re: What are transformer models and how do they work?

#101

Post by HarHarVeryFunny »

p-e-w wrote: post #43
fruit2020 wrote: post #39

Earlier quoted context omitted.

Do you mean that the gpt creators cannot backtrack an answer to understand how the model came up with it? If it’s such a black box how do they evolve it? Trial and error?

They can backtrack it of course, but the result is just billions of numbers – not any sort of "insight".

At the end of the day perhaps the most insight we'll get into why the model is saying what it does will be to ask it! Far from ideal of course, and no better than asking a person why they said/did something (which is often an after-the-fact guess). However, at least any such explanation may be using the same internal model/reasoning as what generated the speech in the first place, so conversational probing may support some sort of triangulation into what was behind it!

Re: What are transformer models and how do they work?

#102

Post by zmmmmm »

james-revisoai wrote: post #76
riffraff wrote: post #28

Earlier quoted context omitted.

Was there any "big idea" after that? It seems most of the user-visible innovation has been "let's use transformers on more data". Perhaps capsule networks? But those are years old too.

RLHF is arguably a bigger jump than LLMs, at least from my perspective beginning to study NLP in 2015/16. Well what exactly is RLHF, practically? The ability to go from 8 google search snippets to correctly rank and rewrite the top one into agreeable, cohesive, grammatical and helpful english is just incredible and allows so much more and the real step change from these models that lead to virality. It also increases…

it's an interesting example of how much seemingly superficial, non-fundamental things can matter.

A lot of AI experts are asserting (probably correctly) that Open AI really has done nothing new and is just putting a shiny sticker on what was already known and published research.

But human perception being what it is, having ChatGPT produce a beautifully formed, polite and friendly sentence seems massively better to lay people than a response that has a more terse, unpolished output. It won't surprise me if there is already a giant layer of heuristics pasted on the end of the Transformer model for ChatGPT cleaning up all sorts of ugly corner cases which researchers would hold highly impure and completely value-less while it actually is responsible for a large amount of ChatGPT's success.

I think there is a bit of a lesson there in terms of how much academia does undervalue the polishing part of research work, even if fundamentals ultimately drive progress.

Re: What are transformer models and how do they work?

#103

Post by p-e-w »

albertzeyer wrote: post #98
ChatGTP wrote: post #73

Earlier quoted context omitted.

The answer is yes. Otherwise the answer should be, we can't really trust the output and it will need to be treated rather suspiciously,just like we have to treat human outputs. At least humans can generally explain their rationale and be hold accountable.

GTP can also explain its reasoning. But that does not tell at all whether this reasoning is really accurate or correct. The same as for humans. When you ask them for some reasoning, they will give you sth, but it doesn't mean that is their real reasoning. There is always a lot of subjective feeling involved which you cannot really formalize. For both GPT and humans. You can't really trust the output of humans. Still,…

As mentioned by GP, though, humans can be held accountable. I believe that is the main reason why people can (sometimes) be trusted: They worry about what will happen to them if they break that trust.

There is no reason to assume that current and future AIs have anything resembling that mechanism.

Re: What are transformer models and how do they work?

#105

Post by Ambix »

p-e-w wrote: post #24

The more I learn about the technical details of how ML systems are implemented, the more I feel that those details obscure , rather than illuminate, what is actually going on. It's as if we were trying to understand human ethics by looking at neurotransmitters or synapses in the brain. These structures seem way too low-level to actually explain the interesting stuff. What I hear is "something something transformer au…

[dead]

Re: What are transformer models and how do they work?

#106

Post by jaidhyani »

isaacfung wrote: post #60
bomewish wrote: post #48

Earlier quoted context omitted.

You seem to know a bunch about this. What’s your rec for best single explainer?

Not the guy you asked, but these are often recommended. https://jalammar.github.io/illustrated-transformer/ https://nlp.seas.harvard.edu/2018/04/03/attention.html

I am the guy asked and I endorse this guy's endorsements.

Re: What are transformer models and how do they work?

#107

Post by jaidhyani »

eiz wrote: post #46
jaidhyani wrote: post #5

Skimming it, there are a few things about this explanation that rub me just slightly the wrong way. 1. Calling the input token sequence a "command". It probably only makes sense to think of this as a "command" on a model that's been fine-tuned to treat it as such. 2. Skipping over BPE as part of tokenization - but almost every transformer explainer does this, I guess. 3. Describing transformers as using a "word embed…

> 4. Describing positional embeddings as multiplicative. They are generally (and very counterintuitively to me, but nevertheless) additive with token embeddings. Worth noting that rotary position embeddings, used in many recent architectures (LLaMA, GPT-NeoX, ...), are very similar to the original sin/cos position embedding in the transformer paper but using complex multiplication instead of addition

TIL. Man, I'm behind on my paper reading.

Re: What are transformer models and how do they work?

#108

Post by jaidhyani »

sillysaurusx wrote: post #19
jaidhyani wrote: post #5

Skimming it, there are a few things about this explanation that rub me just slightly the wrong way. 1. Calling the input token sequence a "command". It probably only makes sense to think of this as a "command" on a model that's been fine-tuned to treat it as such. 2. Skipping over BPE as part of tokenization - but almost every transformer explainer does this, I guess. 3. Describing transformers as using a "word embed…

The positional embedding can be thought of: in the same way you can hear two pieces of music overlaid on each other, you can add both the vocab and pos embedding and it’s able to pick them apart. If you asked yourself to identify when someone’s playing a high note or low note (pos embedding) and whether they’re playing Beethoven or Lady Gaga (vocab embedding) you could do it. That’s why it’s additive and why it would…

Thanks, that's a really useful intuition!

Re: What are transformer models and how do they work?

#109

Post by jaidhyani »

hansvm wrote: post #21
jaidhyani wrote: post #5

Skimming it, there are a few things about this explanation that rub me just slightly the wrong way. 1. Calling the input token sequence a "command". It probably only makes sense to think of this as a "command" on a model that's been fine-tuned to treat it as such. 2. Skipping over BPE as part of tokenization - but almost every transformer explainer does this, I guess. 3. Describing transformers as using a "word embed…

> 6 Selecting the likeliest token is only one of many sampling options, and it's extremely poor for most tasks, moreso when you consider the relationships between multiple executions of the model. _Some_ (not necessarily softmax) probability renormalization trained into the model is issential for a lot of techniques.

That's true, but they didn't go into any other applications in this explainer and were presenting it strictly as a next-word-predictor. If they are going to include final softmax, they should explain why it's useful. It would be improved by being simpler (skip softmax) or more comprehensive (present a use case for softmax), but complexity without reason is bad pedagogy.

Re: What are transformer models and how do they work?

#110

Post by jaidhyani »

pankajdoharey wrote: post #53
indeedmug wrote: post #15

Earlier quoted context omitted.

I found these resources to be helpful. https://jalammar.github.io/illustrated-transformer/ This is a good illustration of the transformer and how the math works. https://karpathy.ai/zero-to-hero.html If you want a deeper understanding of transform and how they fit in the whole picture of deep learning, this series is far and away the best resource I found. Karpathy goes into transformers by the sixth lecture, the pre…

I agree that Karpathy's YouTube video is an excellent resource for understanding Transformers from scratch. It provides a hands-on experience that can be particularly helpful for those who want to implement the models themselves. Here's the link to the video titled "Let's build GPT: from scratch, in code, spelled out": https://youtu.be/kCc8FmEb1nY Additionally, for more comprehensive resources on Transformers, you ma…

I endorse all of this and will further endorse (probably as a follow-up once one has a basic grasp) "A Mathematical Framework for Transformer Circuits" which builds a lot of really useful ideas for understanding how and why transformers work and how to start getting a grasp on treating them as something other than magical black boxes.

https://transformer-circuits.pub/2021/framework/index.html

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