geraneum> This was a clean-room implementation
This is really pushing it, considering it’s trained on… internet, with all available c compilers. The work is already impressive enough, no need for such misleading statements.
The LLM does not contain a verbatim copy of whatever it saw during the pre-training stage, it may remember certain over-represented parts, otherwise it has a knowledge about a lot of things but such knowledge, while about a huge amount of topics, is similar to the way you could remember things you know very well. And, indeed, if you give it access to internet or the source code of GCC and other compilers, it will implement such a project N times faster.
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:
Certified Mitigation of Worst-Case LLM Copyright Infringement
The exposure of large language models (LLMs) to copyrighted material during pre-training raises concerns about unintentional copyright infringement post deployment. This has driven the development of "copyright takedown" methods, post-training approaches aimed at preventing models from generating content substantially similar to copyrighted ones. While current mitigation approaches are somewhat effective for average-case risks, we demonstrate that they overlook worst-case copyright risks exhibits by the existence of long, verbatim quotes from copyrighted sources. We propose BloomScrub, a remarkably simple yet highly effective inference-time approach that provides certified copyright takedown. Our method repeatedly interleaves quote detection with rewriting techniques to transform potentially infringing segments. By leveraging efficient data sketches (Bloom filters), our approach enables scalable copyright screening even for large-scale real-world corpora. When quotes beyond a length threshold cannot be removed, the system can abstain from responding, offering certified risk reduction. Experimental results show that BloomScrub reduces infringement risk, preserves utility, and accommodates different levels of enforcement stringency with adaptive abstention. Our results suggest that lightweight, inference-time methods can be surprisingly effective for copyright prevention.
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?"
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.