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Thanks for stopping by! This is impressive. I would be curious to know if my hunch below about potential weaknesses/tells was at all correct. Did people find it to be as challenging when you showed it to them as some of us are here? Did you expect that level of complexity?
There are likely some "tells" but many fewer of them than I expected. I've seen it occasionally generate something malformed like "#includefrom", and like all GPT2 models it has a tendency to repeat things. Yes, I think people definitely find it challenging. I'm keeping track of the correct and total guesses for each snippet, right now people are at almost exactly 50% accuracy: correct | total | pct ---------+-------…
I managed to get 20/25 correct, and most the wrong ones occurred in the first 10. Once I realized I had to look closer than simple logic errors and slowed way down to read carefully my accuracy increased significantly. Reading the textual parts have the most clues: Comments that didn't match the functionality, variable names that seemed like a soup of related terms, even one typo (separated became separata), debug statements that didn't match the variables they should be outputting. Beyond that the overall cohesiveness was better with real code. When I was a TA in school I used to find that reading through correct code is usually much easier than through incorrect code because you have to start accommodating surreal hypotheticals in your head that might make the code all come together in the end. Disclaimer: I work on NLP projects and I'm quite familiar with the limitations and typical failure modes of transformer models.