3 ms·

I found exact matches. I also found inexact matches, where C functions had been turned into C++ member functions and the like. “Recognized” does not somehow imp

I found exact matches. I also found inexact matches, where C functions had been turned into C++ member functions and the like. “Recognized” does not somehow imply a lack of precision. The LLM the person used was trained on a very large corpus of Open Source code, and reproduced that code exactly. Just like LLMs have reproduced chapters of books and articles from the New York Times exactly.

red75prime 4mo ago
> I found exact matches. Were those functions trivial? With, say, 1% probability of someone who have not seen them writing them like that? > Just like LLMs have reproduced chapters of books and articles from the New York Times exactly. Have you read the articles? As far as I remember they fed large chunks of an article multiple times to an LLM to sometimes get a not-so-long exact match. It can mean that LLMs can infer a style and humans are predictable.
- eschaton 4mo ago
  No, the functions weren’t trivial, and a lot of the surrounding code and structure bore substantial similarities as well. If you saw the two files next to each other, you’d assume it was the result of a copy-paste-adjust process if you didn’t know an LLM was involved.
  - red75prime 4mo ago
    I can only speculate that the model that generated the code hasn't undergone selective unlearning for verbatim data (SUV) or something similar. As you understand "sometimes generates verbatim code" and "just regurgitates [non-trivial] portions its input" are different statements. The possibility of SUV clearly shows that a model does more than "just regurgitating."
- Topfi 4mo ago
  > […] fed large chunks of an article multiple times to an LLM […] So they had to prompt? An LLM? I got this argument before and still don’t get what it’s trying to say. These models do not output anything unless prompted, that’s not any kind of gotcha. On the code outputting front there is a lot of relevant evidence beyond the NYC lawsuit [0]. If I slightly modify GPL code, that doesn’t give me the right to relicense. [0] https://arxiv.org/html/2601.02671?amp=&amp= https://arxiv.org/html/2601.02671?amp=&amp= and https://arxiv.org/abs/2506.12286 https://arxiv.org/abs/2506.12286 and https://ai.stanford.edu/blog/verbatim-memorization/ https://ai.stanford.edu/blog/verbatim-memorization/

red75prime 4mo ago

> I found exact matches. Were those functions trivial? With, say, 1% probability of someone who have not seen them writing them like that? > Just like LLMs have reproduced chapters of books and articles from the New York Times exactly. Have you read the articles? As far as I remember they fed large chunks of an article multiple times to an LLM to sometimes get a not-so-long exact match. It can mean that LLMs can infer a style and humans are predictable.

eschaton 4mo ago
No, the functions weren’t trivial, and a lot of the surrounding code and structure bore substantial similarities as well. If you saw the two files next to each other, you’d assume it was the result of a copy-paste-adjust process if you didn’t know an LLM was involved.
- red75prime 4mo ago
  I can only speculate that the model that generated the code hasn't undergone selective unlearning for verbatim data (SUV) or something similar. As you understand "sometimes generates verbatim code" and "just regurgitates [non-trivial] portions its input" are different statements. The possibility of SUV clearly shows that a model does more than "just regurgitating."
Topfi 4mo ago
> […] fed large chunks of an article multiple times to an LLM […] So they had to prompt? An LLM? I got this argument before and still don’t get what it’s trying to say. These models do not output anything unless prompted, that’s not any kind of gotcha. On the code outputting front there is a lot of relevant evidence beyond the NYC lawsuit [0]. If I slightly modify GPL code, that doesn’t give me the right to relicense. [0] https://arxiv.org/html/2601.02671?amp=&amp= https://arxiv.org/html/2601.02671?amp=&amp= and https://arxiv.org/abs/2506.12286 https://arxiv.org/abs/2506.12286 and https://ai.stanford.edu/blog/verbatim-memorization/ https://ai.stanford.edu/blog/verbatim-memorization/