> I'm curious about why LLM writing fails.
Apart from the tasteless manipulations of the providers, it's mostly training data. LLMs output the average of their training data, and the overwhelming majority of humans are bad writers.
Apart from the tasteless manipulations of the providers, it's mostly training data. LLMs output the average of their training data, and the overwhelming majority of humans are bad writers.
They don’t know what you want to say solely based off a prompt, as it can’t possibly convey enough detail. And they can’t read your mind to fill in the gaps.
If it was just training data, that would actually be a much easier problem to solve.
Obviously llms are far more complex than this, but I think this proves the point. The fact you had to add the “recent” qualifier there highlights that llms in general were bad and had to be provided with corrective targeted training data to improve. (And they still can’t count the R’s in strawberry!)
Really? I do not have the time to survey the modern LLMs to see if your assertion is correct, but if it is then I'm surprised; I would have thought that that one would have shown up so often in their training data that they would be able to answer that question, even if they would then be unable to (for example) count the R's in raspberry, or in some other word where "count the R's in _____" was not widely found in recent online discussion.
My resident Qwythos-9B counts 3 "r"s in "strawberry". So even locally hosted models are catching up.