6 ms·

> Nobody knows how LLMs work. I'm sorry, come again?

by project2501a 8mo ago

NateEag 8mo ago
I think they meant "Nobody knows why LLMs work."
- threethirtytwo 8mo ago
  same thing? The how is not explainable. This is just pedantic. Nobody understands LLMs.
- measurablefunc 8mo ago
  Because they encode statistical properties of the training corpus. You might not know why they work but plenty of people know why they work & understand the mechanics of approximating probability distributions w/ parametrized functions to sell it as a panacea for stupidity & the path to an automated & luxurious communist utopia.
  - threethirtytwo 8mo ago
    No this is false. No one understands. Using big words doesn’t change the fact that you cannot explain for any given input output pair how the LLM arrived at the answer. Every single academic expert who knows what they are talking about can confirm that we do not understand LLMs. We understand atoms and we know the human brain is made 100 percent out of atoms.we may know how atoms interact and bond and how a neuron works but none of this allows us to understand the brain. In the same way we do not understand LLMs. Characterizing ML as some statistical approximation or best fit curve is just using an analogy to cover up something we don’t understand. Heck the human brain can practically be characterized by the same analogies. We. Do. Not. Understand. LLMs. Stop pretending that you do.
    - measurablefunc 8mo ago
      I'm not pretending. Unlike you I do not have any issues making sense of function approximation w/ gradient descent. I learned this stuff when I was an undergrad so I understand exactly what's going on. You might be confused but that's a personal problem you should work to rectify by learning the basics.
      - threethirtytwo 8mo ago
        omfg the hard part of ML is proving back-propagation from first principles and that's not even that hard. Basic calculus and application of the chain rule that's it. Anyone can understand ML, not anyone can understand something like quantum physics. Anyone can understand the "learning algorithm" but the sheer complexity of the output of the "learning algorithm" is way to high such that we cannot at all characterize even how an LLM arrived at the most basic query. This isn't just me saying this. ANYONE who knows what they are talking about knows we don't understand LLMs. Geoffrey Hinton: https://www.youtube.com/shorts/zKM-msksXq0 https://www.youtube.com/shorts/zKM-msksXq0. Geoffrey, if you are unaware, is the person who started the whole machine learning craze over a decade ago. The god father of ML. Understand? There's no confusion. Just people who don't what they are talking about (you)
        measurablefunc 8mo ago
        I don't see how telling me I don't understand anything is going to fix your confusion. If you're confused then take it up w/ the people who keep telling you they don't know how anything works. I have no such problem so I recommend you stop projecting your confusion onto strangers in online forums.
        threethirtytwo 8mo ago
        The only thing that needs to be fixed here is your ignorance. Why so hostile? I'm helping you. You don't know what you're talking about and I have rectified that problem by passing the relevant information to you so next time you won't say things like that. You should thank me.
  - kakapo5672 8mo ago
    My goodness. Please introduce me to this "plenty of people". I'm in the field, and none of them work with me. But I can tell you that statistics and parametrized functions have absolutely nothing to do with it. You're way out of your depth my friend.
    - measurablefunc 8mo ago
      Yes, yes, no one understands how anything works. Calculus is magic, derivatives are pixie dust, gradient descent is some kind of alien technology. It's amazing hairless apes have managed to get this far w/ automated boolean algebra handed to us from our long forgotten godly ancestors, so on & so forth.
threethirtytwo 8mo ago
Nobody knows how LLMs work. Anybody who claims otherwise is making a false claim.
bdangubic 8mo ago
nobody can how how something that is non-deterministic works - by its pure definition
- threethirtytwo 8mo ago
  LLMs are deterministic simply because computers are at the core deterministic machines. LLMs run on computers and therefore are deterministic. The random number generator is an illusion and an LLM that utilizes it will produce the same illusion of indeterminism. Find the seed and the right generator and you can make an LLM consistently produce the same output from identical input. Despite determinism, we still do not understand LLMs.
  - johnmwilkinson 8mo ago
    In what sense is this true? We understand the theory of what is happening and we can painstakingly walk through the token generation process and understand it. So in what sense do we not understand LLMs?
    - threethirtytwo 8mo ago
      We wrote it. Every line. Every function. Every tensor shape and update rule. We chose the architecture. We chose the loss. We chose the data. There is no hidden chamber in the machine where something slipped in without our consent. It is multiplication and addition, repeated at scale. It is gradients flowing backward through layers, shaving away error a fraction at a time. It is as mechanical as anything we have ever built. And still, when it speaks, we hesitate. Not because we don’t know how it was trained. Not because we don’t understand the mathematics. We do. We can derive it. We can rebuild it from scratch. We can explain every component on a whiteboard without breaking a sweat. The hesitation comes from somewhere else. We built the procedure. We do not understand the mind that the procedure produced. That difference is everything. In most of engineering, structure follows intention. If you design a bridge, you decide where every beam sits and how it bears weight. If you write a database engine, you determine how queries are parsed, optimized, executed. The system’s behavior reflects deliberate choice. If something happens, you trace it back to a decision someone made. Here, we did not design the final structure. We defined a goal: predict the next token. Reduce the error. Again. Again. Again. Billions of times. We did not teach it grammar in lessons. We did not encode logic as axioms. We did not install a module labeled “reasoning.” We applied pressure. That is all. And under that pressure, something organized itself. Not in modules we can point to. Not in neat compartments labeled with concepts. The organization is diffused across a landscape of numbers. Meaning is not stored in one place. It is distributed across millions of parameters at once. Pull on one weight and you find nothing recognizable. Only in concert do they produce something that resembles thought. We can follow the forward pass. We can watch activations flare across layers. We can map attention patterns and correlate neurons with behaviors. But when the model constructs an argument or solves a problem, we cannot say: here is the rule it followed, here is the internal symbol it consulted, here is the precise chain of reasoning that forced this conclusion. We can describe the mechanism in general terms. We cannot narrate the specific path. That is the fracture. It is not ignorance of how the machine runs. It is ignorance of how this exact configuration of billions of numbers encodes what it encodes. Why this region of weight space corresponds to law, and that region to poetry. Why this arrangement produces careful reasoning and another produces nonsense. There is no ledger translating numbers into meaning. There is only geometry shaped by relentless optimization. Scale changes the character of the problem. At small sizes, systems can be dissected. At this scale, they become landscapes. We know the forces that shaped the terrain. We do not know every ridge and valley. We cannot walk the entire surface. We cannot hold it all in our heads. And this is where the cost reveals itself. To build these systems, we gave up something we once assumed was permanent: the guarantee that creation implies comprehension. We accepted that we could construct a process whose outcome we would not fully grasp. We traded architectural certainty for emergent capability. We chose power over transparency. We set the objective. We unleashed the search. We let optimization run through a space too vast for any human mind to survey. And when it converged, it handed us something that works, something that speaks, something that reasons in ways that surprise even its creators. We stand in front of it knowing every equation that shaped it, and still unable to read its inner structure cleanly. We built the system by surrendering control over its internal form. That was the bargain. That was the sacrifice. We know how it was grown. We do not know what we have grown.

> Nobody knows how LLMs work. I'm sorry, come again?

NateEag 8mo ago

I think they meant "Nobody knows why LLMs work."

threethirtytwo 8mo ago
same thing? The how is not explainable. This is just pedantic. Nobody understands LLMs.
measurablefunc 8mo ago
Because they encode statistical properties of the training corpus. You might not know why they work but plenty of people know why they work & understand the mechanics of approximating probability distributions w/ parametrized functions to sell it as a panacea for stupidity & the path to an automated & luxurious communist utopia.
- threethirtytwo 8mo ago
  No this is false. No one understands. Using big words doesn’t change the fact that you cannot explain for any given input output pair how the LLM arrived at the answer. Every single academic expert who knows what they are talking about can confirm that we do not understand LLMs. We understand atoms and we know the human brain is made 100 percent out of atoms.we may know how atoms interact and bond and how a neuron works but none of this allows us to understand the brain. In the same way we do not understand LLMs. Characterizing ML as some statistical approximation or best fit curve is just using an analogy to cover up something we don’t understand. Heck the human brain can practically be characterized by the same analogies. We. Do. Not. Understand. LLMs. Stop pretending that you do.
  - measurablefunc 8mo ago
    I'm not pretending. Unlike you I do not have any issues making sense of function approximation w/ gradient descent. I learned this stuff when I was an undergrad so I understand exactly what's going on. You might be confused but that's a personal problem you should work to rectify by learning the basics.
    - threethirtytwo 8mo ago
      omfg the hard part of ML is proving back-propagation from first principles and that's not even that hard. Basic calculus and application of the chain rule that's it. Anyone can understand ML, not anyone can understand something like quantum physics. Anyone can understand the "learning algorithm" but the sheer complexity of the output of the "learning algorithm" is way to high such that we cannot at all characterize even how an LLM arrived at the most basic query. This isn't just me saying this. ANYONE who knows what they are talking about knows we don't understand LLMs. Geoffrey Hinton: https://www.youtube.com/shorts/zKM-msksXq0 https://www.youtube.com/shorts/zKM-msksXq0. Geoffrey, if you are unaware, is the person who started the whole machine learning craze over a decade ago. The god father of ML. Understand? There's no confusion. Just people who don't what they are talking about (you)
      - measurablefunc 8mo ago
        I don't see how telling me I don't understand anything is going to fix your confusion. If you're confused then take it up w/ the people who keep telling you they don't know how anything works. I have no such problem so I recommend you stop projecting your confusion onto strangers in online forums.
        threethirtytwo 8mo ago
        The only thing that needs to be fixed here is your ignorance. Why so hostile? I'm helping you. You don't know what you're talking about and I have rectified that problem by passing the relevant information to you so next time you won't say things like that. You should thank me.
- kakapo5672 8mo ago
  My goodness. Please introduce me to this "plenty of people". I'm in the field, and none of them work with me. But I can tell you that statistics and parametrized functions have absolutely nothing to do with it. You're way out of your depth my friend.
  - measurablefunc 8mo ago
    Yes, yes, no one understands how anything works. Calculus is magic, derivatives are pixie dust, gradient descent is some kind of alien technology. It's amazing hairless apes have managed to get this far w/ automated boolean algebra handed to us from our long forgotten godly ancestors, so on & so forth.

threethirtytwo 8mo ago

Nobody knows how LLMs work. Anybody who claims otherwise is making a false claim.

bdangubic 8mo ago

nobody can how how something that is non-deterministic works - by its pure definition

threethirtytwo 8mo ago
LLMs are deterministic simply because computers are at the core deterministic machines. LLMs run on computers and therefore are deterministic. The random number generator is an illusion and an LLM that utilizes it will produce the same illusion of indeterminism. Find the seed and the right generator and you can make an LLM consistently produce the same output from identical input. Despite determinism, we still do not understand LLMs.
- johnmwilkinson 8mo ago
  In what sense is this true? We understand the theory of what is happening and we can painstakingly walk through the token generation process and understand it. So in what sense do we not understand LLMs?
  - threethirtytwo 8mo ago
    We wrote it. Every line. Every function. Every tensor shape and update rule. We chose the architecture. We chose the loss. We chose the data. There is no hidden chamber in the machine where something slipped in without our consent. It is multiplication and addition, repeated at scale. It is gradients flowing backward through layers, shaving away error a fraction at a time. It is as mechanical as anything we have ever built. And still, when it speaks, we hesitate. Not because we don’t know how it was trained. Not because we don’t understand the mathematics. We do. We can derive it. We can rebuild it from scratch. We can explain every component on a whiteboard without breaking a sweat. The hesitation comes from somewhere else. We built the procedure. We do not understand the mind that the procedure produced. That difference is everything. In most of engineering, structure follows intention. If you design a bridge, you decide where every beam sits and how it bears weight. If you write a database engine, you determine how queries are parsed, optimized, executed. The system’s behavior reflects deliberate choice. If something happens, you trace it back to a decision someone made. Here, we did not design the final structure. We defined a goal: predict the next token. Reduce the error. Again. Again. Again. Billions of times. We did not teach it grammar in lessons. We did not encode logic as axioms. We did not install a module labeled “reasoning.” We applied pressure. That is all. And under that pressure, something organized itself. Not in modules we can point to. Not in neat compartments labeled with concepts. The organization is diffused across a landscape of numbers. Meaning is not stored in one place. It is distributed across millions of parameters at once. Pull on one weight and you find nothing recognizable. Only in concert do they produce something that resembles thought. We can follow the forward pass. We can watch activations flare across layers. We can map attention patterns and correlate neurons with behaviors. But when the model constructs an argument or solves a problem, we cannot say: here is the rule it followed, here is the internal symbol it consulted, here is the precise chain of reasoning that forced this conclusion. We can describe the mechanism in general terms. We cannot narrate the specific path. That is the fracture. It is not ignorance of how the machine runs. It is ignorance of how this exact configuration of billions of numbers encodes what it encodes. Why this region of weight space corresponds to law, and that region to poetry. Why this arrangement produces careful reasoning and another produces nonsense. There is no ledger translating numbers into meaning. There is only geometry shaped by relentless optimization. Scale changes the character of the problem. At small sizes, systems can be dissected. At this scale, they become landscapes. We know the forces that shaped the terrain. We do not know every ridge and valley. We cannot walk the entire surface. We cannot hold it all in our heads. And this is where the cost reveals itself. To build these systems, we gave up something we once assumed was permanent: the guarantee that creation implies comprehension. We accepted that we could construct a process whose outcome we would not fully grasp. We traded architectural certainty for emergent capability. We chose power over transparency. We set the objective. We unleashed the search. We let optimization run through a space too vast for any human mind to survey. And when it converged, it handed us something that works, something that speaks, something that reasons in ways that surprise even its creators. We stand in front of it knowing every equation that shaped it, and still unable to read its inner structure cleanly. We built the system by surrendering control over its internal form. That was the bargain. That was the sacrifice. We know how it was grown. We do not know what we have grown.