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The challenge and danger with widespread use of these tools is that they, with some frequency, continue to just confidently state answers that contain completel
by JCM9 3y ago
The challenge and danger with widespread use of these tools is that they, with some frequency, continue to just confidently state answers that contain completely wrong information. These algorithms are still terrible at just saying I don’t know or here’s a possible answer but I have low confidence this is correct. They give every answer with a tone of authority despite being completely wrong with non-trivial frequency.
A basic search engine search, while much more labor intensive, at least quickly reveals when there’s some inconsistency around a potential topic that allows an inquisitive individual to probe further. These new tools could be super dangerous in that they just spread false information and many folks don’t know or care enough to check if the answers are even right.
We’re going to see a lot more train-wrecks like that lawyer that used ChatGPT to do research on his brief and ended up citing a bunch of cases that were made up and didn’t exist.
- deleted 3y ago[deleted]
- lukebuehler 3y agoThat’s why production RAG systems really require full blown search backends. You decompose the natural language question into a search query with meta data filtering and more. And only then do embeddings search. And only if you get some good search results, with high confidence scores, you use an LLM to summarize or glue the results together, otherwise you branch and tell the user, “I don’t know”. IMO that’s the only way to make it work right now.
- emodendroket 3y agoYeah, that's true. I was doing some research and it _seemed_ really useful how you could say "what's the difference between these two products?" and it would give you a confident answer with comparison tables and everything. At least until you checked and found the specs given were totally fictional.