muskoxnotverydirty

muskoxnotverydirty t1_je027xh wrote

Reply to comment by bjj_starter in [N] OpenAI may have benchmarked GPT-4’s coding ability on it’s own training data by Balance-

Yeah it's speculation. I agree.

> There is no evidence that it was tested on training data, at this point.

I think what the author is trying to say is that for some of these tests there's no evidence it was tested on training data but there's no evidence that it wasn't. But then the ability to generalize in the specific domain of the tests depends on that difference. If nothing else, it would be nice for those who publish test results to show how much they knew whether test data was in the training data. It seems to me that they could automate a search within the training set to see if exact wordage is used.

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muskoxnotverydirty t1_jdwjc1w wrote

Reply to comment by tamilupk in [D] Will prompting the LLM to review it's own answer be any helpful to reduce chances of hallucinations? I tested couple of tricky questions and it seems it might work. by tamilupk

And this method doesn't have some of the drawbacks seen in OP's prompting. Giving an example of an incorrect response followed by self-correction within the prompt may make it more likely that the initial response is wrong, since that's the pattern you're showing it.

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muskoxnotverydirty t1_jdvak20 wrote

Reply to comment by Borrowedshorts in [D]GPT-4 might be able to tell you if it hallucinated by Cool_Abbreviations_9

We've already seen similar prompts such as telling it to say "I don't know" when it doesn't know, and then priming it with examples of it saying "I don't know" to nonsense. Maybe there's something to the added work of getting an output and then iteratively self-critiquing to get to a better final output.

I wonder if they could be using this idea to automatically and iteratively generate and improve their training dataset at scale, which would create a sort of virtuous cycle of improve dataset -> improve LLM -> repeat.

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