There are several types of hallucinations, and the most important one for RAG is grounded factuality.
We built a model to detect this, and it does pretty well! Given a context and a claim, it tells how well the context supports the claim. You can check out a demo at https://playground.bespokelabs.ai
>It's around 500gbs and requires around 300+gbs of vram from my understanding and runs on one of the largest super computers in the world. Sable diffusion has around 6 billion parameters gpt-3/chatgpt has 175 billion.
I am the author of the blog and I want to thank for all the feedback here. Because I now do realize the article starts with a high premise but fails short. Will do a better job next time!
What I meant to talk about is as follows: we have engineered away all sorts of discomfort from our lives and I think that's bad. So (1) be aware of this, (2) seek some discomfort, and (3) if you run into discomfort, take it in a positive way (this I didn't convey in the article).
But yeah I don't mean to say chop of your limbs! Not sure how people are reaching that conclusion.
Here's an example I can think of. Suppose you have a bunch of text documents, and you know that some documents are similar but not identical (e.g. plagiarized and slightly modified). You want to find out which documents are similar.
You can first run the contents through some sort of embedding model (e.g. the recent OpenAI embedding model [1]), and then apply LSH on those embeddings. The documents that have the same LSH value would have had very similar embeddings, and thus very similar content.
I’m guessing by people you mean me? I’m only talking about the state of ChatGPT as per the examples given. I’m not talking about the wider implications into the future or its other amazing capabilities.
Aside: if you scroll to past issues, the author has all sorts of other articles like how to get into p0rn. Wonder if the author picked up AI recently, in which case, that's impressive.
