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ChatGPT Explained: A normie's guide to how it works

jonstokes.com

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Re: ChatGPT Explained: A normie's guide to how it works

#81

Post by marlor »

askvictor wrote: post #78
marlor wrote: post #74

Earlier quoted context omitted.

Previously generated words are added to the input token window. At the point it’s generating the next word, it knows what its preceding words were. With a conceptual representation of various rhyme schemes, subsequent words will (probably) fit that form.

So once it's done one line, when it's writing the next line, it will be stuck with being force to rhyme with whatever it came up with on the first line? Curious what happens when the first line ends in the word 'orange' (or maybe it tries to pick end-of-line-words based on rhyme-ability?)

If certain words (like “orange”) are statistically unlikely to be used in that context in that rhyme scheme, then they’re unlikely to be picked to begin with.

Re: ChatGPT Explained: A normie's guide to how it works

#82

Post by stevenhuang »

ChatGTP wrote: post #30
spion wrote: post #5

I like the token window bit. I don't really like the probability bit, because it kinda alludes that OpenAI just built a huge probability map of all N-grams (N=8000) and called it a day. Which incidentally would also imply that a lot of N-grams just don't exist in the training data, causing the model to completely halt when someone says something unexpected. But that's not the case - instead we convert words into a lo…

So basically, if a pattern that hasn't been encountered before is seen, it will just try to connect "something" together, which is why it does things like predict today's date being in the future etc? The model says, "I don't have a good enough path forwards here, I'll just make one up given the next best thing I have and serve it back"? Maybe this is why Bing is working differently, they've changed the model or the…

Chatgpt is able to caveat it's responses with I don't knows etc, especially when you prompt it appropriately. Tell it not to improvise and provide it escape hatches (if my question doesn't make sense, ask for clarification) and it is able to better recognize when it's about to make something up.

Re: ChatGPT Explained: A normie's guide to how it works

#83

Post by dclowd9901 »

imhoguy wrote: post #6

The biggest drawback of LLM is that it never answers with "I don't know" (unless it is some quote) and it just brings bullshit hallucinations which human has to reject as wrong. Thus it is mostly useless for anything serious. Personally I use it to beautify some text, but still have to do a bit of correction to fix b/s or missed context.

Yeah I don’t understand why “I don’t know, but…” or “I’m not too sure, but…” isn’t part of the generative text. Does everyone on the web talk like they have the answers to everything? I’m genuinely asking.

Re: ChatGPT Explained: A normie's guide to how it works

#84

Post by stevenhuang »

TeeWEE wrote: post #77

I’d explained it in this way: It’s a neural network that learned to understand knowledge by reading a large part of the internet. It’s emergent behavior inside the neural net. It what happens in the brain of a baby. In the first months the eyes can see but the brains cannot. But the data will flow into the brain and due to the learning algorithm: it will start to understand the visual data over time. It’s emergent be…

For more on emergent abilities of LLMs and scaling gains from data and compute, see this great post and discussions on the Chinchilla paper https://www.lesswrong.com/posts/6Fpvch8RR29qLEWNH/chinchilla...

Re: ChatGPT Explained: A normie's guide to how it works

#85

Post by Jensson »

j_crick wrote: post #10
yboris wrote: post #2

I recommend: ChatGPT Is a Blurry JPEG of the Web by Ted Chiang https://www.newyorker.com/tech/annals-of-technology/chatgpt-...

Disagree. As far as I understand, in this article he argues that in the, say, ChatGPT output, compression happens. But does it really? To make a similarly low resolution metaphor, a “bayesian kaleidoscope” of a language model doesn’t necessarily mean it blurs the “word pixels” it is moving around. Because moving them around, rearranging them is what it essentially does, even if in opaque ways; but not degrading them,…

A better metaphor would be to say it compresses the internet, creates a Markov chain based on that compression. Then to make it work it compresses your prompt so that it can find it in the markov chain, move to the next step, and make a lossy decompression into a text token and adds it. The lossy decompression here is the temperature, higher temperature more lossy and more random words, but since it is lossy in the "meaning" space the random words would still have very similar meaning to before.

That isn't a perfect metaphor, but it explains very well how it can do most of the things it can do. The lossy compression means that it can work with large prompts and just capture their essence instead of trying to look them up literally, and the lossy decompression lets it vary its output and the text will move in slightly different directions instead of just repeating text it has seen. The magical bit is that this compression and decompression is much smarter than before, it parses text to a format much closer to its meaning than before, and that lets us do the above much more intelligently.

Edit: Thinking a bit, maybe you could make these model way cheaper to run if we would make them work as a compression to meaning rather than the huge models they are now? They do have internal understanding/meaning of the tokens it gets, so it should be possible to create a compression/decompression function based on these models that transforms text into its world model state, and then once we start working with world model states things should be super cheap relative to what we have now.

Also maybe it doesn't have lossy decompression and get words with similar meaning, but that is another way I see the models could be smaller and cheaper while keeping their essence. The Markov chain step could be all it uses currently. But it definitely creates that space and Markov chain, because it parses the previous thousand or so tokens and uses those to guess the next token, that is a Markov chain. It just has a very sophisticated way of parsing those thousand tokens into a logical format.

Re: ChatGPT Explained: A normie's guide to how it works

#86

Post by Jensson »

starik36 wrote: post #21

ChatGPT is probably the first software product that I have no idea how I'd go about implementing. I watched a number of YT videos about it including Andrew Carpathy's 2 hour coding session building a mini GPT. I understand the process abstractly, but I am unable to grok the details about how it's able to take my vague interpretation of what I want and then write code and actually give me exactly what I wanted.

It parses natural text into a format representing logic, that is the biggest thing to get.

Then once you have such a parser you can now make a logical Markov chain, it predicts the continuation of your text based on continuations of things that looks similar in the logic space rather than the text space, and that is what you get back from the model.

So ChatGPT does what we could have easily done if we had if natural language was more logical. Then we could make a simple model based on all the logic encoded in all writing of humanity that just looks up human done logic similar to what you asked for and then return an average of those logics. Now since you want human language output it now has to translate that back to human text from logic space, and it could be done in English, Spanish, it could speak like a pirate etc.

Re: ChatGPT Explained: A normie's guide to how it works

#87

Post by singularity2001 »

"ChatGPT doesn’t know facts" that's not a helpful statement. A better way to express it would be it doesn't understand its facts or it doesn't grok its knowledge. or maybe the statement is true until it's trained with wikidata on which relationships are Factual.

Re: ChatGPT Explained: A normie's guide to how it works

#90

Post by kybernetikos »

cat_plus_plus wrote: post #89

Who is to say that human brains don't work in a similar way though, with a token window as short term memory and training as long term memory? Even more so when there is injury or disease that prevents long term memories from being formed.

In fact, our short term memory does seem to be token based which is why people talk about "chunking" if you want to keep more info in your working memory.
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