|
|
|
|
|
by Jensson
1208 days ago
|
|
It is trivial to encode text in neural networks, these models try hard to avoid that by punishing it in training but they still encode a lot of text word for word. The most famous example is "Fast inverse square root", it gives you the exact same thing with same comments etc. |
|
The prompt I've seen for it to verbatim reproduce the fast inverse square root from Quake was:
When I ask ChatGPT to give me code for a fast inverse square root it doesn't reproduce it at all but gives me an implementation that looks completely different.So, my original thought was that the prompt above with the characteristic Quake III Q_ naming is enough to push it into a corner where the path is reduced to just one possibility (with that path being the words in the code itself) and not that it merely copypasted the code from an encoded version of it. I.e. it still predicts it word-by-word but with only one possible way for each step. This is just be my naive take on it though but I really want to understand.