Am I reading it correctly or it boils to something along the lines of:
Model is exposed to bad behavior ( backdoor in code ),which colors its future performance?
If yes, this is absolutely fascinating.
Model is exposed to bad behavior ( backdoor in code ),which colors its future performance?
If yes, this is absolutely fascinating.
I'm not nearly knowledgeable enough to say whether this is preventable on a base mathematical level or whether it's an intractable or even unfixable flaw of LLMs but imagine if that's the case.
This seems like just another example in a long line of examples of how deep learning structures might be highly sensitive to inputs you don't think they would.
I suppose it’s interesting in this example but naively, I feel like we’ve seen this behaviour overall from BERT onwards.