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LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

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Re: LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

#121

Post by HarHarVeryFunny »

MichaelZuo wrote: post #102
HarHarVeryFunny wrote: post #55

Earlier quoted context omitted.

> the 60-70B parameters of models is basically like... just stored patterns of "if these 10 tokens in a row input, then these 10 tokens in a row output score the highest" > Is that a good summary? No - there's a lot more going on. It's not just mapping input patterns to output patterns. A good starting point to understand it are linguist's sentence-structure trees (and these were the inspiration for the "transformer"…

Thanks for the explanation. Since unicode has well over 64000 symbols, does that imply models, trained on a large corpus, must necessarily have at least 64000 ‘branches’ at the bottom layer?

The size of the character set (unicode) doesn't really factor into this. Input words are broken down into multi-character tokens (some words will be one token, some split into two, etc), then these tokens mapped into the embedding vectors which is what the model is then operating on.

The linguistic sentence structure tree for any input sentence is a useful way to think about what is happening as the input sentence is fed into the model and processed through it layer by layer, but doesn't have any direct correspondence to the model. The model has a fixed number of layers of fixed max-tokens width, so nothing changes according to the sentence passing through it.

Note that the bottom level of the sentence structure tree is just the words of the sentence, so the number of branches is just the length of the sentence. The model doesn't actually represent these branches though - just the embeddings corresponding to the input, which are transformed from input to output as they are passed through the model and each layer does it's transformer thing.

Re: LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

#122

Post by aldousd666 »

haltIncomplete wrote: post #73
gwervc wrote: post #60

Earlier quoted context omitted.

"We" are not forbidding you to open a computer, start experimenting and publishing some new method. If you're so convinced that "we" are stuck in a local maxima, you can do some of the work you are advocating instead of asking other to do it for you.

All we’re doing is engineering new data compression and retrieval techniques: https://arxiv.org/abs/2309.10668 Are we sure there’s anything “net new” to find within the same old x86 machines, within the same old axiomatic systems of the past? Math is a few operations applied to carving up stuff and we believe we can do that infinitely in theory. So “all math that abides our axiomatic underpinnings” is valid regardles…

Einstein didn't say that about insanity, but... systems exist and are consistently described by particular equations at particular scales. Sure we can say everything is quantum mechanics, even classical physics can technically be translated as a series of wave functions that explain the same behaviors we observe, if we could measure it... But it's impractical, and some of the concepts we think of as fundamental to certain scales, like nucleons, didn't exist at others, like equations that describe the energy of empty space. So, it's maybe not quite a fallacy to point out that not every concept we find to be useful, like deep learning inference, encapsulate every rule at every scale that we know about down to the electrons, cogently. Because none of our theories do that, and even if they did, we couldn't measure or process all the things needed to check and see if we're even right. So we use models that differ from each other, but that emerge from each other, but only when we cross certain scale thresholds.

Re: LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

#123

Post by AnotherGoodName »

Culonavirus wrote: post #37
whatever1 wrote: post #4

Llms seem like a good compression mechanism. It blows my mind that I can have a copy of llama locally on my PC and have access to virtually the entire internet

Yea except it's a lossy compression. With the lost part being hallucinated in at inference time.

Lossy and lossless are way more transferable than people give credit.

Long winded explanation as best as i can in a HN comment. Essentially for state of the art compression both the encoder and the decoder have the same algorithm. They look at the bits encoded/decoded so far, they both run exactly the same prediction on those bits seen so far using some model that predicts based on past data (AI is fantastic for this). If the prediction was 99% likely that the next bit is a '1' the encoder only writes a fraction of a bit to represent that (assuming the prediction is correct) and on the other side the decoder will have the same prediction at that point and either read the next large number of bits to correct or it will be able to simple write '1' to the output and start on the prediction of the next bit given that now written '1'.

Essentially lossy predictions of the next data are great tools to losslessly compress data as those predictions of the next bit/byte/word minimize the data needed to losslessly encode that next bit/byte/word. Likewise you can trivially make a lossy compressor out of a lossless one. Lossy and lossless just aren't that different.

The longstanding Hutter prize for AI in fact judges the AI on how well it can compress data. http://prize.hutter1.net/ This is based in the fact that what we think of as AI and compression are quite interchangeable. There's a whole bunch of papers out on this.

http://prize.hutter1.net/hfaq.htm#compai

I have nothing to do with Hutter but i know all about AI and data compression and their relation.

Re: LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

#124

Post by xyzzy_plugh »

6gvONxR4sf7o wrote: post #106
xanderlewis wrote: post #89

Earlier quoted context omitted.

Which breakthrough in the last two years are you referring to?

If you had to reduce it to one thing, it's probably that language models are capable few shot and zero shot learners. In other words, training a model to simply predict the next word on naturally occurring text, you end up with an tool you can use for generic tasks, roughly speaking.

It turns out a lot of tasks are predictable. Go figure.

Re: LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

#125

Post by advael »

Retric wrote: post #107
victorbjorklund wrote: post #87

Earlier quoted context omitted.

That is how evolution works. Something wins until something else comes along and win. And so on forever.

Evolution generally favors multiple winners in different roles over a single dominate strategy. People tend to favor single winners.

I both think this is a really astute and important observation and also think it's an observation that's more true locally than of people broadly. Modern neoliberal business culture generally and the consolidated current incarnation of the tech industry in particular have strong "tunnel vision" and belief in chasing optimality compared to many other cultures, both extant and past

Re: LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

#126

Post by reverius42 »

pixl97 wrote: post #105
Tijdreiziger wrote: post #101

Earlier quoted context omitted.

You can't have one maxima in the same way you can't have one pencils. That's just how English works.

You can't have one local maxima, it would be the global maxima. So by saying local maxima you're assuming the local is just a piece of a larger whole, even if that global state is otherwise undefined.

No, you can’t have one local maxima, or one global maxima, because it’s plural. You can have one local or global maximum, or two (or more) local or global maxima.

Re: LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

#127

Post by AnotherGoodName »

ramses0 wrote: post #49
bionhoward wrote: post #41

Earlier quoted context omitted.

Yes, that’s known as the Hutter Prize http://prize.hutter1.net/

Not exactly, because LLM's seem to be exhibiting value via "lossy knowledge response" vs. "exact reproduction measured in bytes", but close.

Lossy and lossless are more interchangeable in computer science than people give credit so i wouldn't dwell on that too much. You can optimally convert one into the other with arithmetic coding. In fact the actual best in class algorithms that have won the hutter prize are all lossy behind the scenes. They make a prediction on the next data using a model (often AI based) which is a lossy process and with arithmetic coding they losslessly encode the next data with bits proportional to how correct the prediction was. In fact the reason why the hutter prize is lossless compression is exactly because converting lossy to lossless with arithmetic coding is a way to score how correct a lossy prediction is.

Re: LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

#128

Post by folli »

pixl97 wrote: post #105
Tijdreiziger wrote: post #101

Earlier quoted context omitted.

You can't have one maxima in the same way you can't have one pencils. That's just how English works.

You can't have one local maxima, it would be the global maxima. So by saying local maxima you're assuming the local is just a piece of a larger whole, even if that global state is otherwise undefined.

"You can't have one local pencils, it would be the global pencils"

Re: LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

#129

Post by dragonwriter »

gyrovagueGeist wrote: post #98
erisinger wrote: post #76

Earlier quoted context omitted.

Not a personal attack. These posters are smarter than I am, just ribbing them about misusing the terminology. "Maxima" is plural, "maximum" is singular. So you would say "a local maximum," or "several local maxima." Not "a local maxima" or, the one that really got me, "getting trapped in local maxima's." As for the rest of it, carry on. Good discussion.

While "local maximas" is wrong, I think "a local maxima" is a valid way to say "a member of the set of local maxima" regardless of the number of elements in the set. It could even be a singleton.

No, a member of the set of local maxima is a a local maximum, just like a member of the set of people is a person, because it is a definite singular.

The plural is also used for indefinite number, so “the set of local maxima” remains correct even if the set has cardinality 1, but a member of the set has definite singular number irrespective of the cardinality of the set.

Re: LLMs use a surprisingly simple mechanism to retrieve some stored knowledge

#130

Post by whatever1 »

krainboltgreene wrote: post #44
whatever1 wrote: post #4

Llms seem like a good compression mechanism. It blows my mind that I can have a copy of llama locally on my PC and have access to virtually the entire internet

> have access to virtually the entire internet It isn't even close to 1% of the internet, much less virtually the entire internet. According to the latest dump, Common Crawl has 4.3B pages, but Google in 2016 estimated there are 130T pages. The difference between 130T and 4.3B is about 130T. Even if you narrow it down to Google's searchable text index it's "100's of billions of pages" and roughly 100P compared to Com…

The internet to me and to most of the people is the 10 first search results for the various terms we search for.
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