Smith, Gordon C. S., and Jill P. Pell. 2003. “Parachute Use to Prevent Death and Major Trauma Related to Gravitational Challenge: Systematic Review of Randomised Controlled Trials.” BMJ 327 (7429): 1459–61. https://doi.org/10.1136/bmj.327.7429.1459.

Objectives: To determine whether parachutes are effective in preventing major trauma related to gravitational challenge.

Design: Systematic review of randomised controlled trials.

Data sources: Medline, Web of Science, Embase, and the Cochrane Library databases; appropriate internet sites and citation lists.

Study selection: Studies showing the effects of using a parachute during free fall.

Main outcome measure: Death or major trauma, defined as an injury severity score > 15.

Results: We were unable to identify any randomised controlled trials of parachute intervention.

Conclusions: As with many interventions intended to prevent ill health, the effectiveness of parachutes has not been subjected to rigorous evaluation by using randomised controlled trials. Advocates of evidence based medicine have criticised the adoption of interventions evaluated by using only observational data. We think that everyone might benefit if the most radical protagonists of evidence based medicine organised and participated in a double blind, randomised, placebo controlled, crossover trial of the parachute.

> On average, students spent about 14 HPW preparing for class, 6 HPW working for pay, 12 HPW relaxing and socializing, 7 HPW participating in co-curricular activities and community service, 2 HPW caring for dependents, and 3 HPW commuting to campus. These activities accounted for a total weekly average of 45 HPW.

https://doi.org/10.1007/s11162-018-9497-z

I've looked for other data and have not found anything to indicate the trend has reversed.

Empirically, student free time grew for much of the 20th century, and students spend much less time on school work than they used to. The decline is not explained by students spending more time on work outside of school; much of it has become leisure time.

https://doi.org/10.1162/REST_a_00093 https://doi.org/10.1177/016146811011200908

Now perhaps there has been a sharp change in the years since those two studies (there certainly has been a sharp change in UK tuition costs), but the point is that our intuitions about how hard students must have to work these days are usually wrong.

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.

> 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.