4 ms·

LLMs don’t ’replay variations on prior conversations’ though. Predicting the probable token in a conversation requires predicting the probable subject of the c

LLMs don’t ’replay variations on prior conversations’ though. Predicting the probable token in a conversation requires predicting the probable subject of the conversation, predicting the interlocutors’ relationship and manner of speaking to one another, predicting the state of recollection, preference and taste of the speaker, predicting the speaker’s mental model… If the LLM isn’t predicting all of those things then it will produce poor predictions of the next word; doing it well - and humans tend to agree that in a vast array of cases state of the art LLMs do predict tokens very well - requires that prediction model to predict all that context as well.

mjburgess 2y ago
Alas, no it doesn't. Language induces this sort of anthropomorphism in people, I guess, so consider images. Suppose I take a billion images of all the coffee cups in the world, at a set of angles on the cup, and then build an associative (ie., frequency) statistical model of their pixels (ie., statistical AI). Consider generating one pixel at a time, in sequence, through the image. My associative model tells me P(col of next pixel | all previous). Now, I can generate coffee cups images similar to any variation or combination of the images in the dataset. Now, you might say, "well you can only do that if you have a model of a coffee cup" (rather than of pixels) -- if so, just generate a coffee cup at one of the angles not in the dataset. This will not happen, because the model has not been provided with enough information to do so. Namely, the model does not know the distance from the camera, the camera lens parameters, the angle to the coffee cup, etc. So there's literally a very very large inifinity of possible objects at unseen angles. Consider that underneath a coffee cup, the bottom might be missing entirely, etc. Now it will appear to know all of these things, because its just generating images with these same parameters (camera, angle, distance, etc.). But as soon as you want "a coffee further away than has been seen before", or "a coffee using a macro lens", etc. the whole thing will fall over. It is you, the view, who attributes 3D knowledge to the model because under ordinary circumstances the cause of a photo is features of a 3D environment.
- jameshart 2y ago
  You’re saying this with confidence as if there isn’t a large body of working image and video generation algorithms out there that can produce physically plausible images of objects transposed into circumstances that don’t exist in their training set. A coffee using a macro lens for example. Is it so hard to believe that such models have developed a sense for how light propagates through a scene, a sense for how physical objects change when viewed from different angles, a sense for how lens distortion interacts with light? For goodness’ sake, these same models have a sense of what Greg Rutkowski’s art style is - we are well beyond ‘they’re just remembering pixels from past coffeecups’
  - croon 2y ago
    > Is it so hard to believe that such models have developed a sense for how light propagates through a scene... This specifically is the thing I usually notice in AI images (outside of the hand trope). I'm not GP, and at best a layman in the field, but it's not hard to believe it's possible to generate believable lighting, given enough training data, but if I'm not mistaken it would be through sheer volume of properties like lighting/shadow here usually follows item here. But it's extremely inefficient, and not like we reason. It's like learning the multiplication table without understanding math. Just pairing an infinite amount of properties with each other. We on the other hand develop a grasp of where lighting exists (sun/lamp) and surmise where shadows fall and can muster any image in our mind using that model instead.
  - mjburgess 2y ago
    > it so hard to believe that such models have developed a sense for how light propagates Well, its not a matter or belief or otherwise. I'm a trained practitioner in statistics, AI, physics, and other areas and you can show trivially that you cannot learn light physics from pixel distributions. Pixel distributions aren't stationary, and are caused by a very very large number of factors; likewise the physics of light for any given situation is subject to a large number of causes, all of them entirely absent from from the pixel distributions. This is a pretty trivial thing to show. > have a sense of what Greg Rutkowski’s art style is Well what these models show is that when you have PBs of image data and TBs of associated text data, you can relate words and images together usefully. In particular, you can use patterns of text tokens to sample from image distributions, and combine and vary these samples to produce novel images. The patterns in text and images are caused by people speaking, taking photos, etc. Those patterns necessarily obtain in any generated output. As in, if you train an LLM/etc. on how to speak, using vast amounts of conversational data, it cannot do anything other than appear to speak: that is the only thing the data distribution makes possible. Likewise here, the image generator has a compressed representation of PBs of pixel data which can be sampled from using text. So when you say, "Greg Rutkowski" you select for a highly structured image space, whose structure the original artists placed there. The generative model itself is not imparting structure to the data, it isnt aware of stlyle.. it's sampling from structure that we placed there. When we did so it was because we were, eg., in the room and taking a photo; or imagining what it would be like to apply preraphelite paintaing styles to 60s psychedelic colour pallets because we sensed that fashions of a century ago would now be regarded as cool.
civilized 2y ago
> humans tend to agree that in a vast array of cases state of the art LLMs do predict tokens very well This argument is backwards. Humans don't measure the next token prediction ability of the agents they speak to, human or AI. We rate speakers on whether they seem to understand what we say in context and respond by contributing useful information and analysis. The attributes you're saying can be inferred from known superior next token prediction ability are the things we can actually detect and measure, at least qualitatively. Next token prediction quality is not measurable by humans in any human-meaningful way. Improving test cross entropy by 50% doesn't mean anything to us. It is irrelevant except as a mechanism to train LLMs.
- TeMPOraL 2y ago
  Point is that the simplest way to excel in next token prediction in the way human consider correct - which is rated by how people feel the predictor mimics a human understanding - is to actually have a world model and other components of human understanding. Understanding and compression are the same thing. LLMs are fed a huge chunk of totality of human knowledge, and optimized to compress it well. They for sure aren't doing it by Huffman-encoding a multidimensional lookup table.
  - civilized 2y ago
    > Point is that the simplest way to excel in next token prediction in the way human consider correct - which is rated by how people feel the predictor mimics a human understanding - is to actually have a world model and other components of human understanding. This is a speculative theory for why a next token predictor might sound like it knows what it's talking about. Not something we actually know.
- jameshart 2y ago
  I mean, I think it was implied that humans judge the ‘next token prediction’ ability of LLMs as being good based on the quality of the overall output.
  - civilized 2y ago

mjburgess 2y ago

Alas, no it doesn't. Language induces this sort of anthropomorphism in people, I guess, so consider images. Suppose I take a billion images of all the coffee cups in the world, at a set of angles on the cup, and then build an associative (ie., frequency) statistical model of their pixels (ie., statistical AI). Consider generating one pixel at a time, in sequence, through the image. My associative model tells me P(col of next pixel | all previous). Now, I can generate coffee cups images similar to any variation or combination of the images in the dataset. Now, you might say, "well you can only do that if you have a model of a coffee cup" (rather than of pixels) -- if so, just generate a coffee cup at one of the angles not in the dataset. This will not happen, because the model has not been provided with enough information to do so. Namely, the model does not know the distance from the camera, the camera lens parameters, the angle to the coffee cup, etc. So there's literally a very very large inifinity of possible objects at unseen angles. Consider that underneath a coffee cup, the bottom might be missing entirely, etc. Now it will appear to know all of these things, because its just generating images with these same parameters (camera, angle, distance, etc.). But as soon as you want "a coffee further away than has been seen before", or "a coffee using a macro lens", etc. the whole thing will fall over. It is you, the view, who attributes 3D knowledge to the model because under ordinary circumstances the cause of a photo is features of a 3D environment.

jameshart 2y ago
You’re saying this with confidence as if there isn’t a large body of working image and video generation algorithms out there that can produce physically plausible images of objects transposed into circumstances that don’t exist in their training set. A coffee using a macro lens for example. Is it so hard to believe that such models have developed a sense for how light propagates through a scene, a sense for how physical objects change when viewed from different angles, a sense for how lens distortion interacts with light? For goodness’ sake, these same models have a sense of what Greg Rutkowski’s art style is - we are well beyond ‘they’re just remembering pixels from past coffeecups’
- croon 2y ago
  > Is it so hard to believe that such models have developed a sense for how light propagates through a scene... This specifically is the thing I usually notice in AI images (outside of the hand trope). I'm not GP, and at best a layman in the field, but it's not hard to believe it's possible to generate believable lighting, given enough training data, but if I'm not mistaken it would be through sheer volume of properties like lighting/shadow here usually follows item here. But it's extremely inefficient, and not like we reason. It's like learning the multiplication table without understanding math. Just pairing an infinite amount of properties with each other. We on the other hand develop a grasp of where lighting exists (sun/lamp) and surmise where shadows fall and can muster any image in our mind using that model instead.
- mjburgess 2y ago
  > it so hard to believe that such models have developed a sense for how light propagates Well, its not a matter or belief or otherwise. I'm a trained practitioner in statistics, AI, physics, and other areas and you can show trivially that you cannot learn light physics from pixel distributions. Pixel distributions aren't stationary, and are caused by a very very large number of factors; likewise the physics of light for any given situation is subject to a large number of causes, all of them entirely absent from from the pixel distributions. This is a pretty trivial thing to show. > have a sense of what Greg Rutkowski’s art style is Well what these models show is that when you have PBs of image data and TBs of associated text data, you can relate words and images together usefully. In particular, you can use patterns of text tokens to sample from image distributions, and combine and vary these samples to produce novel images. The patterns in text and images are caused by people speaking, taking photos, etc. Those patterns necessarily obtain in any generated output. As in, if you train an LLM/etc. on how to speak, using vast amounts of conversational data, it cannot do anything other than appear to speak: that is the only thing the data distribution makes possible. Likewise here, the image generator has a compressed representation of PBs of pixel data which can be sampled from using text. So when you say, "Greg Rutkowski" you select for a highly structured image space, whose structure the original artists placed there. The generative model itself is not imparting structure to the data, it isnt aware of stlyle.. it's sampling from structure that we placed there. When we did so it was because we were, eg., in the room and taking a photo; or imagining what it would be like to apply preraphelite paintaing styles to 60s psychedelic colour pallets because we sensed that fashions of a century ago would now be regarded as cool.

civilized 2y ago

> humans tend to agree that in a vast array of cases state of the art LLMs do predict tokens very well This argument is backwards. Humans don't measure the next token prediction ability of the agents they speak to, human or AI. We rate speakers on whether they seem to understand what we say in context and respond by contributing useful information and analysis. The attributes you're saying can be inferred from known superior next token prediction ability are the things we can actually detect and measure, at least qualitatively. Next token prediction quality is not measurable by humans in any human-meaningful way. Improving test cross entropy by 50% doesn't mean anything to us. It is irrelevant except as a mechanism to train LLMs.

TeMPOraL 2y ago
Point is that the simplest way to excel in next token prediction in the way human consider correct - which is rated by how people feel the predictor mimics a human understanding - is to actually have a world model and other components of human understanding. Understanding and compression are the same thing. LLMs are fed a huge chunk of totality of human knowledge, and optimized to compress it well. They for sure aren't doing it by Huffman-encoding a multidimensional lookup table.
- civilized 2y ago
  > Point is that the simplest way to excel in next token prediction in the way human consider correct - which is rated by how people feel the predictor mimics a human understanding - is to actually have a world model and other components of human understanding. This is a speculative theory for why a next token predictor might sound like it knows what it's talking about. Not something we actually know.
jameshart 2y ago
I mean, I think it was implied that humans judge the ‘next token prediction’ ability of LLMs as being good based on the quality of the overall output.
- civilized 2y ago