To make an analogy, right now using a LLM filter to read the literature is like reading Scientific American or New Scientist - fun, interesting, entertaining and not always right on the detail.
Let's say, for example, you wanted to build your own cutting edge LLM - would you just ask an LLM on how to do so? Or would you need to do more, and would a simple literature/internet search be just as effective as a starting point?
Note that in my experience - when you are a world expert in some tiny area ( like when doing a PhD ), you realize that quite a large proportion ( ~50% ) of the stuff published in the area you really know about is either wrong in whole or part, and another good proportion doesn't really move the field on.
So back to the original question - how did OpenAI get a lead in LLM - the story I heard was they talked to leading academic's about who were the best people in the field and tried to hire them all.
ie to paraphrase Richard Feymann on the Emperors nose question - you don't really find out the true answer by averaging over loads of ill-informed opinions - much better to carefully examine the nose/data source yourself.
Let's say, for example, you wanted to build your own cutting edge LLM - would you just ask an LLM on how to do so? Or would you need to do more, and would a simple literature/internet search be just as effective as a starting point?
Note that in my experience - when you are a world expert in some tiny area ( like when doing a PhD ), you realize that quite a large proportion ( ~50% ) of the stuff published in the area you really know about is either wrong in whole or part, and another good proportion doesn't really move the field on.
So back to the original question - how did OpenAI get a lead in LLM - the story I heard was they talked to leading academic's about who were the best people in the field and tried to hire them all.
ie to paraphrase Richard Feymann on the Emperors nose question - you don't really find out the true answer by averaging over loads of ill-informed opinions - much better to carefully examine the nose/data source yourself.