Off the thousands of responses I have read from the top LLMs in the last couple of years: never seen one that was creative. Throwing writing, coding, problem solving, mathematical questions and what not.
It's somewhat easier to perceive the creativeless aspect with stable diffusion. I'm not talking about the missing limb or extra finger glitches. With a bit of experience looking through generated images our brain eventually perceives the absolute lack of creativity, an artist probably spot it without prior experience with generative AI pieces. With LLMs it takes a bit longer.
Anecdotal, baseless I guess. Papers were published, some researchers in the fields of science couldn't get the best LLMs to solve any unsolved problem. I recently came across a paper stating bluntly that all LLMs tested were unable to conceptualize, nor derive laws that generalize whatsoever. E.g formulas.
We are being duped, it doesn't help selling $200 monthly subscriptions - soon for even more - if marketers admitted there is absolutely zero reasoning going on with these stochastic machines on steroids.
I deeply wish the circus ends soon, so that we can start focusing on what LLMs are excellent, well fitted to do better, faster than humans.
Everyone I talked to, knowledgeable in machine learning and/or deep learning, who had no reason to pretend of course, agreed an LLM is a stochastic machines. That it is couples with very good other NLP techniques doesn't change that.
It is why even the best models today miss the shot by a large margin, then hit a decent match. Again back to the creative issue. If it was done before, a good input and a well trained model on good data will output good data likely to match with the best (matching) answer ever produced. Some NLP to make it sound unique is not equal to creativity.
Off the thousands of responses I have read from the top LLMs in the last couple of years: never seen one that was creative. Throwing writing, coding, problem solving, mathematical questions and what not.
It's somewhat easier to perceive the creativeless aspect with stable diffusion. I'm not talking about the missing limb or extra finger glitches. With a bit of experience looking through generated images our brain eventually perceives the absolute lack of creativity, an artist probably spot it without prior experience with generative AI pieces. With LLMs it takes a bit longer.
Anecdotal, baseless I guess. Papers were published, some researchers in the fields of science couldn't get the best LLMs to solve any unsolved problem. I recently came across a paper stating bluntly that all LLMs tested were unable to conceptualize, nor derive laws that generalize whatsoever. E.g formulas.
We are being duped, it doesn't help selling $200 monthly subscriptions - soon for even more - if marketers admitted there is absolutely zero reasoning going on with these stochastic machines on steroids.
I deeply wish the circus ends soon, so that we can start focusing on what LLMs are excellent, well fitted to do better, faster than humans.
Creative it is not.
It doesn't need to be creative, it needs to brute force boilerplate.
An opinion on the current state of the field. The usual stochastic parrot mention. That, I see. Reasons for the existence of the wall? Not so much.
That it is usual doesn't mean it's false.
Everyone I talked to, knowledgeable in machine learning and/or deep learning, who had no reason to pretend of course, agreed an LLM is a stochastic machines. That it is couples with very good other NLP techniques doesn't change that.
It is why even the best models today miss the shot by a large margin, then hit a decent match. Again back to the creative issue. If it was done before, a good input and a well trained model on good data will output good data likely to match with the best (matching) answer ever produced. Some NLP to make it sound unique is not equal to creativity.