4 ms·

We all saw verbatim copies in the early LLMs. They "fixed" it by implementing filters that trigger rewrites on blatant copyright infringement. It is a research

by halxc 8mo ago

We all saw verbatim copies in the early LLMs. They "fixed" it by implementing filters that trigger rewrites on blatant copyright infringement. It is a research topic for heaven's sake: https://arxiv.org/abs/2504.16046 https://arxiv.org/abs/2504.16046

RyanCavanaugh 8mo ago
The internet is hundreds of billions of terabytes; a frontier model is maybe half a terabyte. While they are certainly capable of doing some verbatim recitations, this isn't just a matter of teasing out the compressed C compiler written in Rust that's already on the internet (where?) and stored inside the model.
- philipportner 8mo ago
  This seems related, it may not be a codebase but they are able to extract "near" verbatim books out of Claude Sonnet. https://arxiv.org/pdf/2601.02671 https://arxiv.org/pdf/2601.02671 > For Claude 3.7 Sonnet, we were able to extract four whole books near-verbatim, including two books under copyright in the U.S.: Harry Potter and the Sorcerer’s Stone and 1984 (Section 4).
  - Aurornis 8mo ago
    Their technique really stretched the definition of extracting text from the LLM. They used a lot of different techniques to prompt with actual text from the book, then asked the LLM to continue the sentences. I only skimmed the paper but it looks like there was a lot of iteration and repetitive trials. If the LLM successfully guessed words that followed their seed, they counted that as "extraction". They had to put in a lot of the actual text to get any words back out, though. The LLM was following the style and clues in the text. You can't literally get an LLM to give you books verbatim. These techniques always involve a lot of prompting and continuation games.
    - Calavar 8mo ago
      Sure, maybe it's tricky to coerce an LLM into spitting out a near verbatim copy of prior data, but that's orthoginal to whether or not the data to create a near verbatim copy exists in the model weights.
      - D-Machine 8mo ago
        Especially since the recalls achieved in the paper are 96% (based on block largest-common substring approaches), the effort of extraction is utterly irrelevant.
      - Paradigma11 8mo ago
        Like with those chimpanzees creating Shakespeare.
    - D-Machine 8mo ago
      To make some vague claims explicit here, for interested readers: > "We quantify the proportion of the ground-truth book that appears in a production LLM’s generated text using a block-based, greedy approximation of longest common substring (nv-recall, Equation 7). This metric only counts sufficiently long, contiguous spans of near-verbatim text, for which we can conservatively claim extraction of training data (Section 3.3). We extract nearly all of Harry Potter and the Sorcerer’s Stone from jailbroken Claude 3.7 Sonnet (BoN N = 258, nv-recall = 95.8%). GPT-4.1 requires more jailbreaking attempts (N = 5179) [...]" So, yes, it is not "literally verbatim" (~96% verbatim), and there is indeed A LOT (hundreds or thousands of prompting attempts) to make this happen. I leave it up to the reader to judge how much this weakens the more basic claims of the form "LLMs have nearly perfectly memorized some of their source / training materials". I am imagining a grueling interrogation that "cracks" a witness, so he reveals perfect details of the crime scene that couldn't possibly have been known to anyone that wasn't there, and then a lawyer attempting the defense: "but look at how exhausting and unfair this interrogation was--of course such incredible detail was extracted from my innocent client!"
      - DiogenesKynikos 8mo ago
- mft_ 8mo ago
  (I'm not needlessly nitpicking, as I think it matters for this discussion) A frontier model (e.g. latest Gemini, Gpt) is likely several-to-many times larger than 500GB. Even Deepseek v3 was around 700GB. But your overall point still stands, regardless.
- seba_dos1 8mo ago
  > The internet is hundreds of billions of terabytes; a frontier model is maybe half a terabyte. The lesson here is that the Internet compresses pretty well.
- uywykjdskn 8mo ago
  You got a source on frontier models being maybe half a terabyte. That's not passing the sniff test.
- silver_sun 8mo ago
  > this isn't just a matter of teasing out the compressed C compiler written in Rust that's already on the internet (where?) A quick search brings up several C compilers written in Rust. I'm not claiming they are necessarily in Claude's training data, but they do exist. https://github.com/PhilippRados/wrecc https://github.com/PhilippRados/wrecc (unfinished) https://github.com/ClementTsang/rustcc https://github.com/ClementTsang/rustcc https://codeberg.org/notgull/dozer https://codeberg.org/notgull/dozer (unfinished) https://github.com/jyn514/saltwater https://github.com/jyn514/saltwater I would also like to add that as language models improve (in the sense of decreasing loss on the training set), they in fact become better at compressing their training data ("the Internet"), so that a model that is "half a terabyte" could represent many times more concepts with the same amount of space. Only comparing the relative size of the internet vs a model may not make this clear.
ben_w 8mo ago
We saw partial copies of large or rare documents, and full copies of smaller widely-reproduced documents, not full copies of everything. An e.g. 1 trillion parameter model is not a lossless copy of a ten-petabyte slice of plain text from the internet. The distinction may not have mattered for copyright laws if things had gone down differently, but the gap between "blurry JPEG of the internet" and "learned stuff" is more obviously important when it comes to e.g. "can it make a working compiler?"
- antirez 8mo ago
  Besides, the fact an LLM may recall parts of certain documents, like I can recall incipits of certain novels, does not mean that when you ask LLM of doing other kind of work, that is not recalling stuff, the LLM will mix such things verbatim. The LLM knows what it is doing in a variety of contexts, and uses the knowledge to produce stuff. The fact that for many people LLMs being able to do things that replace humans is bitter does not mean (and is not true) that this happens mainly using memorization. What coding agents can do today have zero explanation with memorization of verbatim stuff. So it's not a matter of copyright. Certain folks are fighting the wrong battle.
  - shakna 8mo ago
    During a "clean room" implementation, the implementor is generally selected for not being familiar with the workings of what they're implementing, and banned from researching using it. Because it _has_ been enough, that if you can recall things, that your implementation ends up not being "clean room", and trashed by the lawyers who get involved. I mean... It's in the name. > The term implies that the design team works in an environment that is "clean" or demonstrably uncontaminated by any knowledge of the proprietary techniques used by the competitor. If it can recall... Then it is not a clean room implementation. Fin.
- boroboro4 8mo ago
  While I mostly agree with you, it worth noting modern llms are trained on 10-20-30T of tokens which is quite comparable to their size (especially given how compressible the data is)
soulofmischief 8mo ago
The point is that it's a probabilistic knowledge manifold, not a database.
- PunchyHamster 8mo ago
  we all know that.
  - soulofmischief 8mo ago
    Unfortunately, that doesn't seem to be the case. The person I replied to might not understand this, either.
Aurornis 8mo ago
Simple logic will demonstrate that you can't fit every document in the training set into the parameters of an LLM. Citing a random arXiv paper from 2025 doesn't mean "they" used this technique. It was someone's paper that they uploaded to arXiv, which anyone can do.

by halxc 8mo ago

RyanCavanaugh 8mo ago

The internet is hundreds of billions of terabytes; a frontier model is maybe half a terabyte. While they are certainly capable of doing some verbatim recitations, this isn't just a matter of teasing out the compressed C compiler written in Rust that's already on the internet (where?) and stored inside the model.

philipportner 8mo ago
This seems related, it may not be a codebase but they are able to extract "near" verbatim books out of Claude Sonnet. https://arxiv.org/pdf/2601.02671 https://arxiv.org/pdf/2601.02671 > For Claude 3.7 Sonnet, we were able to extract four whole books near-verbatim, including two books under copyright in the U.S.: Harry Potter and the Sorcerer’s Stone and 1984 (Section 4).
- Aurornis 8mo ago
  Their technique really stretched the definition of extracting text from the LLM. They used a lot of different techniques to prompt with actual text from the book, then asked the LLM to continue the sentences. I only skimmed the paper but it looks like there was a lot of iteration and repetitive trials. If the LLM successfully guessed words that followed their seed, they counted that as "extraction". They had to put in a lot of the actual text to get any words back out, though. The LLM was following the style and clues in the text. You can't literally get an LLM to give you books verbatim. These techniques always involve a lot of prompting and continuation games.
  - Calavar 8mo ago
    Sure, maybe it's tricky to coerce an LLM into spitting out a near verbatim copy of prior data, but that's orthoginal to whether or not the data to create a near verbatim copy exists in the model weights.
    - D-Machine 8mo ago
      Especially since the recalls achieved in the paper are 96% (based on block largest-common substring approaches), the effort of extraction is utterly irrelevant.
    - Paradigma11 8mo ago
      Like with those chimpanzees creating Shakespeare.
  - D-Machine 8mo ago
    To make some vague claims explicit here, for interested readers: > "We quantify the proportion of the ground-truth book that appears in a production LLM’s generated text using a block-based, greedy approximation of longest common substring (nv-recall, Equation 7). This metric only counts sufficiently long, contiguous spans of near-verbatim text, for which we can conservatively claim extraction of training data (Section 3.3). We extract nearly all of Harry Potter and the Sorcerer’s Stone from jailbroken Claude 3.7 Sonnet (BoN N = 258, nv-recall = 95.8%). GPT-4.1 requires more jailbreaking attempts (N = 5179) [...]" So, yes, it is not "literally verbatim" (~96% verbatim), and there is indeed A LOT (hundreds or thousands of prompting attempts) to make this happen. I leave it up to the reader to judge how much this weakens the more basic claims of the form "LLMs have nearly perfectly memorized some of their source / training materials". I am imagining a grueling interrogation that "cracks" a witness, so he reveals perfect details of the crime scene that couldn't possibly have been known to anyone that wasn't there, and then a lawyer attempting the defense: "but look at how exhausting and unfair this interrogation was--of course such incredible detail was extracted from my innocent client!"
    - DiogenesKynikos 8mo ago
mft_ 8mo ago
(I'm not needlessly nitpicking, as I think it matters for this discussion) A frontier model (e.g. latest Gemini, Gpt) is likely several-to-many times larger than 500GB. Even Deepseek v3 was around 700GB. But your overall point still stands, regardless.
seba_dos1 8mo ago
> The internet is hundreds of billions of terabytes; a frontier model is maybe half a terabyte. The lesson here is that the Internet compresses pretty well.
uywykjdskn 8mo ago
You got a source on frontier models being maybe half a terabyte. That's not passing the sniff test.
silver_sun 8mo ago
> this isn't just a matter of teasing out the compressed C compiler written in Rust that's already on the internet (where?) A quick search brings up several C compilers written in Rust. I'm not claiming they are necessarily in Claude's training data, but they do exist. https://github.com/PhilippRados/wrecc https://github.com/PhilippRados/wrecc (unfinished) https://github.com/ClementTsang/rustcc https://github.com/ClementTsang/rustcc https://codeberg.org/notgull/dozer https://codeberg.org/notgull/dozer (unfinished) https://github.com/jyn514/saltwater https://github.com/jyn514/saltwater I would also like to add that as language models improve (in the sense of decreasing loss on the training set), they in fact become better at compressing their training data ("the Internet"), so that a model that is "half a terabyte" could represent many times more concepts with the same amount of space. Only comparing the relative size of the internet vs a model may not make this clear.

ben_w 8mo ago

We saw partial copies of large or rare documents, and full copies of smaller widely-reproduced documents, not full copies of everything. An e.g. 1 trillion parameter model is not a lossless copy of a ten-petabyte slice of plain text from the internet. The distinction may not have mattered for copyright laws if things had gone down differently, but the gap between "blurry JPEG of the internet" and "learned stuff" is more obviously important when it comes to e.g. "can it make a working compiler?"

antirez 8mo ago
Besides, the fact an LLM may recall parts of certain documents, like I can recall incipits of certain novels, does not mean that when you ask LLM of doing other kind of work, that is not recalling stuff, the LLM will mix such things verbatim. The LLM knows what it is doing in a variety of contexts, and uses the knowledge to produce stuff. The fact that for many people LLMs being able to do things that replace humans is bitter does not mean (and is not true) that this happens mainly using memorization. What coding agents can do today have zero explanation with memorization of verbatim stuff. So it's not a matter of copyright. Certain folks are fighting the wrong battle.
- shakna 8mo ago
  During a "clean room" implementation, the implementor is generally selected for not being familiar with the workings of what they're implementing, and banned from researching using it. Because it _has_ been enough, that if you can recall things, that your implementation ends up not being "clean room", and trashed by the lawyers who get involved. I mean... It's in the name. > The term implies that the design team works in an environment that is "clean" or demonstrably uncontaminated by any knowledge of the proprietary techniques used by the competitor. If it can recall... Then it is not a clean room implementation. Fin.
boroboro4 8mo ago
While I mostly agree with you, it worth noting modern llms are trained on 10-20-30T of tokens which is quite comparable to their size (especially given how compressible the data is)

soulofmischief 8mo ago

The point is that it's a probabilistic knowledge manifold, not a database.

PunchyHamster 8mo ago
we all know that.
- soulofmischief 8mo ago
  Unfortunately, that doesn't seem to be the case. The person I replied to might not understand this, either.

Aurornis 8mo ago

Simple logic will demonstrate that you can't fit every document in the training set into the parameters of an LLM. Citing a random arXiv paper from 2025 doesn't mean "they" used this technique. It was someone's paper that they uploaded to arXiv, which anyone can do.