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this post was submitted on 02 Aug 2023
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I don't understand why they don't use a second model to detect falsehoods instead of trying to fix it in the original LLM?
And then they can use a third model to detect falsehoods in the second model and a fourth model to detect falsehoods in the third model and... well, it's LLMs all the way down.
The LLM Centipede
Token Ring AI
Ai models are already computationally intensive. This would instantly double the overhead. Also being able to detect problems does not mean you're able to fix them.
More than double, as query size is very much connected to the effective cost of the generation, and you'd need to include both the query and initial response in that second pass.
Then - you might need to make an API call to a search engine or knowledge DB to fact check it.
And include that data as context along with the query and initial response to whatever decides if it's BS.
So for a dumb realtime chat application, no one is going to care enough to slow out down and exponentially increase costs to avoid hallucinations.
But for AI replacing a $120,000 salaried role in writing up a white paper on some raw data analysis, a 10-30x increase over a $0.15 query is more than acceptable.
So you will see this approach taking place in enterprise scenarios and professional settings, even if we may never see them in chatbots.
Cause what are you gonna train the second model on? Same data as the first just recreates it and any other data is gonna be nice and mucky with all the ai content out there
Because then they still need a reliable method to detect falsehoods. That's the issue here.
2+ times the cost for every query for something that makes less than 5% unusable isn't a trade off that people are willing to make for chat applications.
This is the same fix approach for jailbreaking.
You absolutely will see this as more business critical integrations occur - it just still probably won't be in broad consumer facing realtime products.