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How LLMs work

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Re: How LLMs work

#251

Post by rfv6723 »

0xbadcafebee wrote: post #209
rfv6723 wrote: post #62

Earlier quoted context omitted.

Is it different though? If we look beyond written languages which are late inventions of human civilization, oral languages are continuous and build with blocks not words. Chomskyan school misled the entire field of linguistics for decades by ignoring spoken languages.

Chomsky did the opposite of what you're saying. He didn't ignore spoken language. He said that human vocalization is independent of language, and that the way our brains can manipulate and use sound (a cognitive capability, not specifically an aural one) is the fundamental differentiator that allows us to make compound ideas, and our specific use of language is a byproduct. Example: a programming language's capabilit…

[flagged]

Re: How LLMs work

#252

Post by Vachyas »

jochembrouwer wrote: post #234
ekunazanu wrote: post #76

Earlier quoted context omitted.

> This was openai’s entire breakthrough. Making this particular model architecture larger leads to emergent capabilities Basically, the bitter lesson: https://www.cs.utexas.edu/~eunsol/courses/data/bitter_lesson...

So the take-away here is that we (as humans) try to model these AIs like humans, but eventually these AIs get better. Which to me seems like a logical conclusion if they can do "things" (like "learning" or pattern matching) much faster than we can (the compute). Then language in LLMs is a bottleneck, the AI is constrained by the language, and thus if we want to scale further we could let AI create its own language (w…

I think one hypothesis along these lines is that, if allowed, due to the limitations of human language you described, LLMs will gravitate towards "inventing" their own language (which, due to training pressures, may even resemble english from the outside, but contain deeper, "true", meaning within), but that we should do our best to prevent this even if it bottlenecks reasoning capabilities since it would cut off our ability to read its "true" thoughts and detect misalignment

See: https://openai.com/index/chain-of-thought-monitoring/

Quote below:

  Chain-of-thought (CoT) reasoning models “think” in natural language understandable by humans. Monitoring their “thinking” has allowed us to detect misbehavior such as subverting tests in coding tasks, deceiving users, or giving up when a problem is too hard.

  We believe that CoT monitoring may be one of few tools we will have to oversee superhuman models of the future.

  We have further found that directly optimizing the CoT to adhere to specific criteria (e.g. to not think about reward hacking) may boost performance in the short run; however, it does not eliminate all misbehavior and can cause a model to hide its intent. We hope future research will find ways to directly optimize CoTs without this drawback, but until then

  We recommend against applying strong optimization pressure directly to the CoTs of frontier reasoning models, leaving CoTs unrestricted for monitoring.

  We understand that leaving CoTs unrestricted may make them unfit to be shown to end-users, as they might violate some misuse policies. Still, if one wanted to show policy-compliant CoTs directly to users while avoiding putting strong supervision on them, one could use a separate model, such as a CoT summarizer or sanitizer, to accomplish that.

Re: How LLMs work

#253

Post by layla5alive »

spaceisballer wrote: post #232
Gareth321 wrote: post #218

Earlier quoted context omitted.

I’m kind of amazed when I read comments like this, but I have to remind myself that I work in an industry which use these tools at the cutting edge and see what they can really do. In the space of 18 months I changed from skeptic to the belief that our world is going to RAPIDLY change, and soon. I sense that statistics and benchmarks and research and statements from the world’s greatest academics won’t sway you, so m…

I have no doubt there are similar stories to yours or to a lesser extent. And while I’m glad you have gotten some relief, what price are we paying as a society? I’m not just talking about the environmental impact alone, what about the societal impact? Social media has not been around too long but it seems to be a net negative for society. I’m sure there are plenty of anecdotes to the contrary, but the studies are sho…

I think most of the nuance in your first argument was undermined and overshadowed by your fairly categorical assertion that LLMs are bullshit generators and not actually useful.

When you got a reply illustrating how incorrect that claim from your first argument was, you shifted to focusing on the other argument (the one I actually happen to agree with - the cost to society of hitching increasing dependency on big tech will make the social media harms look like childs play).

I think your argument will be better received if you focus on the very valid concerns of societal harms, and acknowledge the ways LLMs are tremendously capable, without downplaying that.

I'm with the person you replied to in seeing how capable LLMs can be when you spar with them appropriately. They confabulate, but that's your job to catch as a sparring partner. But they do bring useful knowledge of thousands of PhDs into conversations - and even if you're among the most erudite humans on the planet, this is still an asset in intellectual search for truth on many topics.

Back to the genuine problems, and they are many: this power, concentrated in the hands of big tech, is a multiplier on the power already concentrated there, with many new capabilities - especially scary being the capabilities for subtly influencing and manipulating both individual and group behavior - for profit or otherwise - by the companies or their customers, or governments, or... The possibility space of harm and abuse is large..

On net, I think we should all be pushing to educate everyone around us on the pros, the nuances, the risks, and the big cons, and working to try to build a future of offline models rather than subscription service dependency..

Re: How LLMs work

#254

Post by rramadass »

Any article titled "How LLMs work" (or similar) and which does not start with conditional and joint probability (high-level and not necessarily detailed) and then show by hand how a trivial language with tokens (eg: "the", "mat", "cat", "sat", "on") can produce the semantically coherent and most likely sentence (eg. "The Cat sat on the Mat") is no good. The intuition for the whole should be built using the above before diving into details of transformers etc.

Re: How LLMs work

#255

Post by tinktank »

LatencyKills wrote: post #144
crossroadsguy wrote: post #86

Earlier quoted context omitted.

What hopes/paths does a mere CS bachelor (not deep into stats/maths), and mid level dev (native mobile only; 10-15 years exp.), have about not only understanding it (maybe not fully) but getting possibly into this as a career? Not expecting churning out models and AI systems from the first weeks/months but entry/employment into this field? (If I can be honest, and I am not being disparaging about anything lest it mig…

I have a BS in CS (and have been in the field for 25 years). I couldn't understand the transformer architecture until I built a few myself. Here are the books I worked through. I now feel I have a very good understanding of modern LLMs. https://www.amazon.com/Build-Large-Language-Model-Scratch/dp... https://www.amazon.com/Build-DeepSeek-Scratch-Abhijit-Dandek...

Has it given you enough of an understanding that you can pick up and follow research papers or did you have to do more to achieve that?

Re: How LLMs work

#256

Post by [deleted] »

giardini wrote: post #198
helloplanets wrote: post #87

The part about positional encoding is not correct. > The intuition: instead of adding position info to each token’s vector, RoPE rotates the vector by an angle that depends on its position You can't rotate the token's entire vector (or all three vectors, whatever is being implied is unclear). You rotate each token's Query and Key vectors only, so dot product can be used to tell how far apart the tokens are when compa…

Could you restate this another way: I don't follow.

[deleted]

Re: How LLMs work

#257

Post by aaroninsf »

rhubarbtree wrote: post #227
helloplanets wrote: post #87

The part about positional encoding is not correct. > The intuition: instead of adding position info to each token’s vector, RoPE rotates the vector by an angle that depends on its position You can't rotate the token's entire vector (or all three vectors, whatever is being implied is unclear). You rotate each token's Query and Key vectors only, so dot product can be used to tell how far apart the tokens are when compa…

Yep, you’re correct. I got to that bit and thought that can’t be right. It’s obviously wrong. If you rotate a semantic vector, you change the semantics of it. You don’t want that. Makes me wonder if the whole thing is just slop. Is the rest of the article correct? Anyone suggest an alternative article?

The repetition of some phrases/statements means it's either poorly edited, machine generated, or both.

Re: How LLMs work

#258

Post by Mali- »

vocram wrote: post #68

Saying an article is of inferior quality just because editing was AI-assisted is like saying a book is lower quality just because it was printed rather than written by hand

People think AI is a bad editor (I personally don't), here's your argument under a particularly harsh version of that framing:

Saying an article is of inferior quality just because editing was done by my 13 year old nephew is like saying a book is lower quality just because it was printed rather than written by hand

Obviously, this doesn't work because the content is changing. Printing a piece vs writing it by hand doesn't change the content, only the medium of transmission.

Re: How LLMs work

#259

Post by locknitpicker »

galaxyLogic wrote: post #216
locknitpicker wrote: post #181

Earlier quoted context omitted.

> The secret sauce though is all the datasets, RL training, knowledge of what works from doing all kinds of ablation experiments, and a massive compute moat. ReAct loops and tool-calling are the critical development feature. They turn a model from something that generates text into something that can independently influence the world around them. Without agent features, you have just a chatbot.

The big breakthrough is we can interact with the agents using natural language - because of the LLM. It is the combination of LLM and agent-harnesses that make it look really smart. Agent-harness is a programmatic device that lets us tap into the vast knowledge in the LLM. It is probabaly true that many TV-commentators fail to appreciate this fact and therefore think LLMs are super-intelligent. No, it is the combinat…

> The big breakthrough is we can interact with the agents using natural language - because of the LLM.

Without ReAct and tool calling, all you have is a chatbot. That's useful, but it's just a chatbot.

ReAct loops and tool calling is what unblocks high value usecases. It enables systems to actually address free-form problem statements, gather data that is not a part of their training set, inspect the current state of services,and trigger actions in external systems. This goes well beyond mere chatbots.

> It is the combination of LLM and agent-harnesses that make it look really smart.

It's really not about "smart". It's about autonomous systems, and being able to consume and analyze new data, and trigger actions in external systems.

Re: How LLMs work

#260

Post by Borealid »

hackinthebochs wrote: post #248
Borealid wrote: post #240

Earlier quoted context omitted.

The point is that the output is text that is statistically correlated with the input. The capability of the LLM is not to reason, it's to generate text that matches the patterns seen in the training corpus. It's possible that all you need to "reason" is plausible text generation. I'm not saying it's not. But nothing the LLM does fails to be explained by plausible-text-generation. I contend that the best way to unders…

>The point is that the output is text that is statistically correlated with the input. But we can simply note that this description applies to any machine learning algorithm. Yet LLMs are lightyears better than, say, Markov chains. What people are after is something that elucidates the features of LLMs that allow them to be so productive over what came before.

There is absolutely nothing stopping someone from distilling a modern LLM into a very effective Markov chain. The physical size of the model would explode because a context window containing C tokens of size B would need B^C Markov prior states, but the actual output would be a deterministic version of the LLM's with top-n n=1 sampling.

In other words, a Markov chain and a Transformer model are exactly equivalent in power (there is NOTHING that can be done with one and not the other). The Transformer model is just better pretrained and a more efficient compression/generation.

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