This is a massive simplification. It's adequate for most purposes, but when discussing at this level this simplification breaks down.
Tokens are stored, and the NN contains weights of likelihood of a token occurring after another, given a sequence of prior (and possibly post) tokens.
Verbatim retrieval usually means there is very little variation in the training data for that sequence, so the same set of weights gets stored.
So "retrieval" is actually the same generative process as unique code uses, but the NN hasn't seen any other versions.
Sounds like there is still a db lookup, just not at runtime and instead at build time of the NN. Can you clarify this please?
The issue here is that certain sentences (code segments) are memorized, and reproduced -- much like a language learner who completes every sentence which begins with "Mi nombre" with the phrase "Mi nombre es Mark". The regurgitation is based on high probability built into the priors, not an explicit lookup. A different logit sampling method (instead of taking the likeliest) reduces regurgitation, without changing anything else about the network. (It also makes nonsense happen more often, since nonsense items are inherently less likely!)
There is no database lookup.
I've attempted to break that part down here: https://news.ycombinator.com/item?id=27744156
But you seem to have a basic fundamental misunderstanding of what is going on inside the NN. There is no "search for code" - it is generating new code each time, but sometimes that code will be the same as something it has seen because there is little or no variation in the training data for that snippet.
The NN generates code token by token, conditioned on the code leading up to it (and perhaps the code ahead, similar to BERT).
If you see tokens like this you probably generate the same next token too:
You have conditioned your input on the code you have seen and the most likely token you produce is ":".That's what the NN does, but for much longer range conditioning.