> TerraPower must still complete construction, submit an operating license application, and satisfy all applicable safety and regulatory requirements before loading fuel and beginning operations.

Paragraph 4.3.3:

> While data indicated that portable electronic devices were more often the cause of fire in aircraft cabins than power banks were, the latter were a significant concern due to their increased use and a prevalence of lower-quality products with defects or vulnerabilities that were more likely to lead to thermal events. Power banks were also not offered the same level of protection that batteries installed in portable electronic devices were provided. The amendments therefore focused on power banks.

1. The post mainly reiterates a single idea (Capsicum enumerates what the process can do, seccomp provides a configurable filter) in many different ways. There is not much actual depth, code samples notwithstanding. Nothing on why different designs were chosen, how easy each is to use, outcomes besides the Chrome example, etc.

2. There are a lot of AI writing tells, like staccato sentences, parallelism ("Same browser. Same threat model. Same problem."), pointless summary tables, "it's not X, it's Y" contradiction ("This is not a bug. It is the original Unix security model"), etc.

3. The author has roughly a blog post a day, all with similar style and on widely varied topics, and in the same writing style. Unless the author has deep expertise on a remarkably wide range of topics and spends all their time writing, these can't reflect deep insight or experience, but minimal editing of AI output.

So yes, it's pretty sloppy.

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

That was with GPT4, but my own work with other LLMs show they have very distinctive styles even if you specifically prompt them with a chunk of human text to imitate. I think instruction-tuning with tasks like summarization predisposes them to certain grammatical structures, so their output is always more information-dense and formal than humans.

The smarter journals have started accepting submissions in any format on the first round, and then only require enough formatting for the typesetters to do their job.

Accessibility in PDFs is also very difficult. I'm not sure any publishers are yet meeting PDF/UA-2 requirements for tagged PDFs, which include things like embedding MathML representations of all mathematics so screenreaders can parse the math. LaTeX only supports this experimentally, and few other tools support it at all.

Our experience (https://arxiv.org/abs/2410.16107) is that LLMs like GPT-4o have a particular writing style, including both vocabulary and distinct grammatical features, regardless of the type of text they're prompted with. The style is informationally dense, features longer words, and favors certain grammatical structures (like participles; GPT-4o loooooves participles).

With Llama we're able to compare base and instruction-tuned models, and it's the instruction-tuned models that show the biggest differences. Evidently the AI companies are (deliberately or not) introducing particular writing styles with their instruction-tuning process. I'd like to get access to more base models to compare and figure out why.

Comparing base and instruction-tuned models, the base models are vaguely human in style, while instruction-tuned models systematically prefer certain types of grammar and style features. (For example, GPT-4o loves participial clauses and nominalizations.) https://arxiv.org/abs/2410.16107

When I've looked at more recent models like o3, there are other style shifts. The newer OpenAI models increasingly use bold, bulleted lists, and headings -- much more than, say, GPT-3.5 did.

So you get what you optimize for. OpenAI wants short, punchy, bulleted answers that sound authoritative, and that's what they get. But that's not how humans write, and so it'll remain easy to spot AI writing.

edit: yeah, ran it through our style feature tagger and nothing jumps out. Low rate of nominalizations (ChatGPT loves those), only a few present participles, "that" as subject at a usual rate, usual number of adverbs, etc. (See table 3 of the paper.) No contractions, which is unusual for normal human writing but common when assuming a more formal tone. I think the author has just affected a particular style, perhaps deliberately.

The power does not extend to any of your hypotheticals, which are not about active cases. Courts do not accept cases on the grounds that some bad thing might happen in the future; the plaintiff must show some concrete harm has already occurred. The only thing different here is how much potential evidence OpenAI has been asked to retain.

Students emerge from lectures with a bunch of vague, partly contradictory, partly incorrect ideas in their head. They generally aren't aware of this and think the lecture "made sense." Then they start the homework and find they must translate those vague ideas into extremely precise code so the computer can do it -- forcing them to realize they do not understand, and forcing them to make the vague understanding concrete.

If they ask an AI to write the code for them, they don't do that. Annotating has some value, but it does not give them the experience of seeing their vague understanding run headlong into reality.

I'd expect the result to be more like what happens when you show demonstrations to students in physics classes. The demonstration is supposed to illustrate some physics concept, but studies measuring whether that improves student understanding have found no effect: https://doi.org/10.1119/1.1707018

What works is asking students to make a prediction of the demonstration's results first, then show them. Then they realize whether their understanding is right or wrong, and can ask questions to correct it.

Post-hoc rationalizing an LLM's code is like post-hoc rationalizing a physics demo. It does not test the students' internal understanding in the same way as writing the code, or predicting the results of a demo.