> Well, what do you expect? LLMs are trained on blithering, mostly from web sites. So you get blithering out.

I realize this isn't entirely serious, but I can't resist pointing out that this doesn't seem to be a good explanation for why LLMs write the way they do. When we've experimented with LLM writing style on open-weights models where you can get a base model (pretraining on text only) and an instruction-tuned variant (pretraining + post-training with RLHF and whatever other human-evaluated tasks), it's the instruction-tuned variant that shows the weird writing quirks. That is, the writing style is not because of the training texts, but because of whatever tasks the LLM companies do in instruction tuning. https://arxiv.org/abs/2410.16107

I'd speculate that this is partly impressed human preferences (the human raters unintentionally reward a particular writing style) and partly because of the chosen tasks: they're training the LLM to be good at, say, summarizing text, so it develops a style that's good at being informationally dense.

At any rate I've seen this same phenomenon with Llama and Gemma, and will be trying soon with Qwen. Unfortunately none of the commercial models lets you access the base model, as far as I know.