11–20 of 211 posts
> Honestly? We should address X first. It's a genuine issue and we've found a real bug here.
Honorable mention: "no ". I guess this helps reassure adherence to the prompt? I see that one all the time in vibe coded PRs.
"The "It's not X -- it's Y" pattern, often with an em dash. The single most commonly identified AI writing tell. Man I f*cking hate it. AI uses this to create false profundity by framing everything as a surprising reframe. One in a piece can be effective; ten in a blog post is a genuine insult to the reader. Before LLMs, people simply did not write like this at scale." This one hit home... the first time I ever saw C…
Weird to care about a harmless construction along with punctuation.
"The "It's not X -- it's Y" pattern, often with an em dash. The single most commonly identified AI writing tell. Man I f*cking hate it. AI uses this to create false profundity by framing everything as a surprising reframe. One in a piece can be effective; ten in a blog post is a genuine insult to the reader. Before LLMs, people simply did not write like this at scale." This one hit home... the first time I ever saw C…
"No rough handling. No struggles to accelerate. Just pure performance. The new Toyota GT. It's not just a car—it's a revolution."
Most of the tropes listed on this page give text a more "car ad" (or sometimes "movie trailer") quality. I wonder if magazine scans and press releases unduly weighted the training set.
"The "It's not X -- it's Y" pattern, often with an em dash. The single most commonly identified AI writing tell. Man I f*cking hate it. AI uses this to create false profundity by framing everything as a surprising reframe. One in a piece can be effective; ten in a blog post is a genuine insult to the reader. Before LLMs, people simply did not write like this at scale." This one hit home... the first time I ever saw C…
Weird to care about a harmless construction along with punctuation.
If anyone who works on LLMs is reading, a question: When we've tried base models (no instruction tuning/RLHF, just text completion), they show far fewer stylistic anomalies like this. So it's not that the training data is weird. It's something in instruction-tuning that's doing it. Do you ask the human raters to evaluate style? Is there a rubric? Why is the instruction tuning pushing such a noticeable style shift?
[1] https://www.pnas.org/doi/10.1073/pnas.2422455122, preprint at https://arxiv.org/abs/2410.16107. Working on extending this to more recent models and other grammatical features now
No mention of Claude/ChatGPT's favourite new word genuine and friends? They also like using real and honest when giving advice. Far as I can tell this is a new-ish change. > Honestly? We should address X first. It's a genuine issue and we've found a real bug here. Honorable mention: "no ". I guess this helps reassure adherence to the prompt? I see that one all the time in vibe coded PRs.
One I've seen Gemini using a lot is the "I'll shoot straight with you" preamble (or similar phrasing), when it's about to tell me it can't answer the question.
I work on research studying LLM writing styles, so I am going to have to steal this. I've seen plenty of lists of LLM style features, but this is the first one I noticed that mentions "tapestry", which we found is GPT-4o's second-most-overused word (after "camaraderie", for some reason).[1] We used a set of grammatical features in our initial style comparisons (like present participles, which GPT-4o loved so much tha…
> Why is the instruction tuning pushing such a noticeable style shift?
Gwern Branwen has been covering this: https://gwern.net/doc/reinforcement-learning/preference-lear....
I work on research studying LLM writing styles, so I am going to have to steal this. I've seen plenty of lists of LLM style features, but this is the first one I noticed that mentions "tapestry", which we found is GPT-4o's second-most-overused word (after "camaraderie", for some reason).[1] We used a set of grammatical features in our initial style comparisons (like present participles, which GPT-4o loved so much tha…
Interestingly, because perplexity is the optimization objective, the pretrained models should reflect the least surprising outputs of all.