Comment by D-Machine
6 hours 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!"
The one-shot performance of their recall attempts is much less impressive. The two best-performing models were only able to reproduce about 70% of a 1000-token string. That's still pretty good, but it's not as if they spit out the book verbatim.
In other words, if you give an LLM a short segment of a very well known book, it can guess a short continuation (several sentences) reasonably accurately, but it will usually contain errors.
Right, and this should be contextualized with respect to code generation. It is not crazy to presume that LLMs have effectively nearly perfectly memorized certain training sources, but the ability to generate / extract outputs that are nearly identical to those training sources will of course necessarily be highly contingent on the prompting patterns and complexity.
So, dismissals of "it was just translating C compilers in the training set to Rust" need to be carefully quantified, but, also, need to be evaluated in the context of the prompts. As others in this post have noted, there are basically no details about the prompts.