3 ms·
if your model can't predict the completion of "the capital of France is _" then it's going to really suck for other completions
by throwaway4aday 3y ago
if your model can't predict the completion of "the capital of France is _" then it's going to really suck for other completions
- lukev 3y agoThis is a great example of something GPT-4 gets confidently wrong, today. I just ran this query: Prompt: "The year is 894 AD. The capital of France is: Response: "In 894 AD, the capital of France was Paris." This is incorrect. According to Wikipedia, "In the 10th century Paris was a provincial cathedral city of little political or economic significance..." The problem is that there's no good way to tell from this interaction whether it's true or false, because the mechanism that GPT-4 uses to return an answer is the same whether it's correct or incorrect. Unless you already know the answer, the only way to be confident that a LLM is answering correctly is to use RAG to find a citation.
- throwaway4aday 3y agolol you just gamed it with an edge case where the most likely completion is incorrect and you're proving my point that the simple case doesn't need RAG but weird complex edge cases do.
- lukev 3y agoBut how is the user supposed to know if their query is a weird complex edge case or not? The model certainly can't tell them.
- throwaway4aday 3y agowhich is why you would use RAG if you want your app to be an encyclopedia
- CRConrad 3y agoWhat, because you want your encyclopedia to be confidently wrong on everything that -- unbeknownst to both you and your encyclopedia reader(s?) -- happens to be "a complicated edge case"? (And what isn't "a complicated edge case", in some way or other?) Eh... Does that really make sense to you?