I agree, but it’s worth noting that that has been done since long before LLMs. Fifteen years ago, I used to teach a graduate course on academic writing pedagogy. The students and I would read research papers on the teaching of academic writing; we also analyzed textbooks and course syllabuses to get an idea about what was actually being done in classrooms. While phrases like “critical thinking” did come up, the overall focus was clearly on language patterns: sentence and paragraph structure, the use of transition words, vocabulary for hedging and boosting (i.e., making assertions seem weaker or stronger), etc.

In a university context, it can be very difficult to evaluate student writing based on its content. In humanities-focused and creative writing, what the student decides to say can be seen as an extension of the student’s personality, identity, and individual experience; if a teacher evaluates the content, including the reasoning, it can seem that the teacher is evaluating the student as a person. And if the students are in the sciences, especially at the graduate level, the writing teacher often won’t even understand what the students write because it is too technical. Teaching and evaluating language patterns, not content, is often the only option.

This is honestly both terrifying and well articulated.

High praise to the blog author.

I think that the current models are still like over-achieving savants rather than true human level because the largest model is only 1/10th the complexity of the human brain. I've recently become fairly convinced that new hardware paradigms (like types of CIM) are about to move from research into real-world development and scaling. So I believe within a few years, the model sizes will increase by another 10 times.

Compared to upcoming 100 trillion parameter models, humans will obviously be _much_ dumber/slower than AI in all fields. Already with the 10T models, some LLMs beat 99.9% of humans in competitive programming.

The AI hatred from many may actually continue to increase, but in cases where the bottom line matters, we are rapidly approaching the point where writing or work product that looks like it is human-authored will be suspect just on that basis. In other words, for some people it will be the reverse -- "this work looks like it was created by a human" could be devastating for your businesses credibility at that point.

I don't know if it was written that way to show trust in the reader's intelligence, show disregard for reaching a wide audience, show a demonstration of skill, or was artifact of someone just thinking at that level.

This feels like an easy enough hypothesis to verify, for anyone in the business of training LLMs - does the not-X-but-Y rate increase after RLVR?

It's interesting why LLMs generate constructions like this more frequently than they presumably exist in the training set. I wonder if this is some sort of mode collapse caused by post training, and/or maybe because they are training on synthetic data so these things become self-perpetuating and self-amplifying (a feedback loop)?

The lesson for humans worried about being falsely identified as AI is just learn to write better! It doesn't matter where your repertoire of phrasing comes from (copying AI or not), but one of the basic rules of writing is not to repeat yourself unless you are doing so deliberately for a purpose. Go ahead and use "It's not just X. It's Y" if you want to, but if you use it multiple times in the same short piece of writing, then you may deserve to be called out for poor style, if not for being an AI.

Sometimes it’s not just about the Ys but also the Qs.

How is this different from humans? When I went to high school, my teachers extorted me too. Especially subjects like English and unlike Math, where evaluation is 100% subjective.

- "No X, No Y, No Z." pattern

- "Here is X - it makes Y"

The worst and most obvious one is the constant over use of emoji ticks and crosses.

It is bad writing.