This could end up being another example of knowledge quality going down as people keep going for the easiest was to get answers, like searching for info on topics on Instagram and TikTok.
Post train one LLM to please another LLM, that rates the quality of the first model's responses and calls it on any bullshit! (And vice versa.)
On the other hand, diffusion models seem to have replaced GANs for image synthesis, so perhaps there's something I'm missing, or perhaps there's a way to combine both techniques.
Although an adversarial extra-finger detector seems in order! Extra fingers are not creativity. They are taboo!!
But for text, there is a lot of structure around bullshitting, and fortunately, the internet is full of examples of people calling bullshit. As long as the bullshit adversary has to give a strong critique to back up any bullshit call, it should work.
And bullshit judgments can be de-bullshitted too.
This would be the same as what our social circles, work colleagues, families, etc. do for us, so a circular firing squad of bullshit judges, who also give gold stars for quality responses, seems like a natural solution for models too.
I'm not worried about using approaches like this to converge on truth (model A generates an answer to a question, model B attempts to refute model A). Perhaps we could train model A to generate lies that model B can't detect.
I see this a lot on social media, but it simply isn't true in my experience as someone who uses multiple APIs from OpenAI.
Totally absurd question, totally absurd answer.
"Hello! Can you prove how a martingale converges to the zariski measure on a commutative scheme?"
It replied:
"Hello! The notion of a martingale converging to the Zariski measure on a commutative scheme is a highly advanced and abstract mathematical concept. It is not possible to provide a simple proof for this statement, as it requires a deep understanding of several advanced mathematical concepts such as probability theory, algebraic geometry, and commutative algebra."
It then listed definitions for martingale, Zariski measure, and a commutative scheme, and then concluded with:
"However, to fully understand this concept, you would need to study advanced mathematical topics such as algebraic geometry, commutative algebra, and probability theory."
That seems like a useful enough answer with definitions that effectively converges to "I don't know."
Sorry saw your reply too late.
My usage has been confined to specific technical inquiries and chatgpts benefit has been colossal in saving me the time of sifting through decade(s) of documentation, forum posts, bug reports, kb articles, etc.
I will be doing some testing of this against the base davinci model over the next few weeks.
(Unfortunately I can't provide you an example. The ChatGPT history is not available right now. Wed Mar 8 02:02:14 PM CET 2023)
I think in the future it'll become less problematic or more transparent about hallucinations