No, they're not padentic or "anoying". Words have meaning. The more precise their use is, the clearer the communication becomes. Investing the time to properly learn pre-established termilology has tremendous value for everyone. Well, everyone but those who are too lazy and sloppy, and would rather just confuse everybody instead.

I'm glad this disgusting sloppiness doesn't exist in more established fields. I hope people keep calling it out.

It's valuable data sometimes. Those types of comments show you how other people perceived something.

In this case my lens is colored by my interests; I've been thinking a lot on RL for LLMs and it slipped out here. For me I rely on phrases to quickly filter for (subjectively) interesting work. And assuming we value that subjective filtering of content, we make it easier for others to filter consistently when we use consistent language.

Among arxiv publications there are 217 results that contain "large language model" in the full text and "from scratch" in the title or abstract.

There are 2873 results that contain "large language model" in the full text and use "pretrained" in the title or abstract. A 10x difference in publication count does make one feel more common than the other?

I'd need to get into more involved queries to break down the semantic categories of those papers.