It depends. The base model, sure you can't really figure out what percentage of it came from which data source since there's just too many data sources and that information is lost along the way. They're likely not using the entirety of SO to generate answers though. Retraining LLMs is ungodly expensive, so they can't retrain it every time a new Q or A is created, and even retraining on a regular basis would be impractical.

Instead, without knowing exactly how they're doing it of course, my guess is they're pulling relevant Q&As from their database, then using those results to improve the response (for example by providing them as context). If you're interested, look into retrieval-augmented generation.

Posters aren't saying that its impossible to put search results through an LLM and ask it to cite the source it reads. They're saying that the neural networks, as used today in LLMs, do not store token attribution in the vocabulary or per node. You can implement a system for the neural network to work in that provides it the proper input (search results) and prodding (a prompt that encourages the network to biasing toward citation), not that the single LLM can conceptualize of that on its own.

Thanks for the TLDW - I could ogle a bit of the article but since I was at work, I couldn't just play the video out loud.

If it's doing a search for the code, pulling it in to the context, and then spitting it back out in slightly modified form, then it can attribute the source it pulled in. That's a very different thing from the AI because code that is pulled into context by a search had a strong influence on the output. The output is still generated the same way but it would be reasonable to credit the author of the code that is pulled in. However, the code in the training data cannot be credited. How you would pull in just the right piece of code in the first place though is a bit of a mystery to me.