3 ms·

I like the analogy of compression, in that a distilled model of an LLM is like a JPEG of a photo. Pretty good, maybe very good, but still lossy. The question

by MR4D 2y ago

I like the analogy of compression, in that a distilled model of an LLM is like a JPEG of a photo. Pretty good, maybe very good, but still lossy. The question I hear you raising seems to be along the lines of, can we use a new compression method to get better resolution (reproducibility of the original) in a much smaller size.

umeshunni 2y ago
> in that a distilled model of an LLM is like a JPEG of a photo That's an interesting analogy, because I've always thought of the hidden states (and weights and biases) of an LLMs as a compressed version of the training data.
- kedarkhand 2y ago
  Well, JPEG can be thought of as an compression of the natural world of whose photograph was taken
  - bloomingkales 2y ago
    And we can answer the question why quantization works with a lossy format, since quantization just drops accuracy for space but still gives us a good enough output, just like a lossy jpeg. Reiterating again, we can lose a lot of data (have incomplete data) and have a perfectly visible jpeg (or MP3, same thing).
- homarp 2y ago
  hence https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web https://www.newyorker.com/tech/annals-of-technology/chatgpt-... (by Ted Chiang) (discussed here: https://news.ycombinator.com/item?id=34724477 https://news.ycombinator.com/item?id=34724477 )
- timschmidt 2y ago
  And what is compression but finding the minimum amount of information required to reproduce a phenomena? I.e. discovering natural laws.
  - t_mann 2y ago
    Finding minimum complexity explanations isn't what finding natural laws is about, I'd say. It's considered good practice (Occam's razor), but it's often not really clear what the minimal model is, especially when a theory is relatively new. That doesn't prevent it from being a natural law, the key criterion is predictability of natural phenomena, imho. To give an example, one could argue that Lagrangian mechanics requires a smaller set of first principles than Newtonian, but Newton's laws are still very much considered natural laws.
    - timschmidt 2y ago
      Maybe I'm just a filthy computationalist, but the way I see it, the most accurate model of the universe is the one which makes the most accurate predictions with the fewest parameters. The Newtonian model makes provably less accurate predictions than Einsteinian (yes, I'm using a different example), so while still useful in many contexts where accuracy is less important, the number of parameters it requires doesn't much matter when looking for the one true GUT. My understanding, again as a filthy computationalist, is that an accurate model of the real bonafide underlying architecture of the universe will be the simplest possible way to accurately predict anything. With the word "accurately" doing all the lifting. As always: https://www.sas.upenn.edu/~dbalmer/eportfolio/Nature%20of%20Science_Asimov.pdf https://www.sas.upenn.edu/~dbalmer/eportfolio/Nature%20of%20... I'm sure there are decreasingly accurate, but still useful, models all the way up the computational complexity hierarchy. Lossy compression is, precisely, using one of them.
      - t_mann 2y ago
        The thing is, Lagrangian mechanics makes exactly the same predictions as Newtownian, and it starts from a foundation of just one principle (least action) instead of three laws, so it's arguably a sparser theory. It just makes calculations easier, especially for more complex systems, that's its raison d'être. So in a world where we don't know about relativity yet, both make the best predictions we know (and they always agree), but Newton's laws were discovered earlier. Do they suddenly stop being natural laws once Lagrangian mechanics is discovered? Standard physics curricula would not agree with you btw, they practically always teach Newtownian mechanics first and Lagrangian later, also because the latter is mathematically more involved.
ziofill 2y ago
What you say makes sense, but is there the possibility that because it’s compressed it can generalize more? In the spirit of bias/variance.
cmgriffing 2y ago
This brings up an interesting thought too. A photo is just a lossy representation of the real world. So it's lossy all the way down with LLMs, too. Reality > Data created by a human > LLM > Distilled LLM
fennecfoxy 2y ago
Yeah but it does seem that they're getting high % numbers for the distilled models accuracy against the larger model. If the smaller model is 90% as accurate as the larger, but uses much < 90% of the parameters, then surely that counts as a win.

by MR4D 2y ago

umeshunni 2y ago

> in that a distilled model of an LLM is like a JPEG of a photo That's an interesting analogy, because I've always thought of the hidden states (and weights and biases) of an LLMs as a compressed version of the training data.

kedarkhand 2y ago
Well, JPEG can be thought of as an compression of the natural world of whose photograph was taken
- bloomingkales 2y ago
  And we can answer the question why quantization works with a lossy format, since quantization just drops accuracy for space but still gives us a good enough output, just like a lossy jpeg. Reiterating again, we can lose a lot of data (have incomplete data) and have a perfectly visible jpeg (or MP3, same thing).
homarp 2y ago
hence https://www.newyorker.com/tech/annals-of-technology/chatgpt-is-a-blurry-jpeg-of-the-web https://www.newyorker.com/tech/annals-of-technology/chatgpt-... (by Ted Chiang) (discussed here: https://news.ycombinator.com/item?id=34724477 https://news.ycombinator.com/item?id=34724477 )
timschmidt 2y ago
And what is compression but finding the minimum amount of information required to reproduce a phenomena? I.e. discovering natural laws.
- t_mann 2y ago
  Finding minimum complexity explanations isn't what finding natural laws is about, I'd say. It's considered good practice (Occam's razor), but it's often not really clear what the minimal model is, especially when a theory is relatively new. That doesn't prevent it from being a natural law, the key criterion is predictability of natural phenomena, imho. To give an example, one could argue that Lagrangian mechanics requires a smaller set of first principles than Newtonian, but Newton's laws are still very much considered natural laws.
  - timschmidt 2y ago
    Maybe I'm just a filthy computationalist, but the way I see it, the most accurate model of the universe is the one which makes the most accurate predictions with the fewest parameters. The Newtonian model makes provably less accurate predictions than Einsteinian (yes, I'm using a different example), so while still useful in many contexts where accuracy is less important, the number of parameters it requires doesn't much matter when looking for the one true GUT. My understanding, again as a filthy computationalist, is that an accurate model of the real bonafide underlying architecture of the universe will be the simplest possible way to accurately predict anything. With the word "accurately" doing all the lifting. As always: https://www.sas.upenn.edu/~dbalmer/eportfolio/Nature%20of%20Science_Asimov.pdf https://www.sas.upenn.edu/~dbalmer/eportfolio/Nature%20of%20... I'm sure there are decreasingly accurate, but still useful, models all the way up the computational complexity hierarchy. Lossy compression is, precisely, using one of them.
    - t_mann 2y ago
      The thing is, Lagrangian mechanics makes exactly the same predictions as Newtownian, and it starts from a foundation of just one principle (least action) instead of three laws, so it's arguably a sparser theory. It just makes calculations easier, especially for more complex systems, that's its raison d'être. So in a world where we don't know about relativity yet, both make the best predictions we know (and they always agree), but Newton's laws were discovered earlier. Do they suddenly stop being natural laws once Lagrangian mechanics is discovered? Standard physics curricula would not agree with you btw, they practically always teach Newtownian mechanics first and Lagrangian later, also because the latter is mathematically more involved.

ziofill 2y ago

What you say makes sense, but is there the possibility that because it’s compressed it can generalize more? In the spirit of bias/variance.

cmgriffing 2y ago

This brings up an interesting thought too. A photo is just a lossy representation of the real world. So it's lossy all the way down with LLMs, too. Reality > Data created by a human > LLM > Distilled LLM

fennecfoxy 2y ago

Yeah but it does seem that they're getting high % numbers for the distilled models accuracy against the larger model. If the smaller model is 90% as accurate as the larger, but uses much < 90% of the parameters, then surely that counts as a win.