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>Hallucinations are less of an issue because you can just dump all the source data in, whole codebases can go in for stuff ranging from a refactor to ‘write doc
by simion314 2y ago
>Hallucinations are less of an issue because you can just dump all the source data in, whole codebases can go in for stuff ranging from a refactor to ‘write documentation of the API’.
Is there no risk ? I mean say for testing purposes we give the AI a giant CSV file and ask it to make it a json is the chance for error 0% ? Because today we need to double check when we ask AI to transform some data or transform some code, there is the risk of it messing something up but if it is not something that would crash immediately you risk introducing a ton of new bugs by asking an AI to refactor instead of using some good tools.
- vessenes 2y agoThere's always risk! But when you ask a model to rely on just the input data, you are (mostly) trying to tap into its reasoning, not knowledge side. Obviously what's kind of magical is that some knowledge will be needed for reasoning, and you have it. But LLMs small and large are pretty good at doing the in-context stuff. It is precisely what they're trained on, and in fact it was kind of a surprise how well they seemed to generalize outside of this task in the first place.