Being invited to conference talks around the world is a completely normal part of being an active researcher in almost any academic field, so it doesn't register as pompous to other academics.
> TerraPower must still complete construction, submit an operating license application, and satisfy all applicable safety and regulatory requirements before loading fuel and beginning operations.
> 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.
Probably. One common feature of LLM output is grammatical features that indicate information density, like nominalizations, longer words, participial clauses, and so on. Perhaps training tasks that involve asking the LLMs for concise explanations or summaries encourage the use of these features to give denser answers.
I've heard the Kenya and Nigeria story, but has anyone backed it up with quantitative evidence that the vocabulary LLMs overuse coincides with the vocabulary that is more common in Kenyan and Nigerian English than in American English?
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 that they were a pretty accurate classifier on their own), but it shouldn't be too hard to pattern-match some of these other features and quantify them.
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?
No, that doesn't really work so well. A lot of the LLM style hallmarks are still present when you ask them to write in another style, so a good quantitative linguist can find them: https://hdsr.mitpress.mit.edu/pub/pyo0xs3k/release/2
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 first sentence is a reference to prior research work that has found those productivity gains, not a summary of the experiment conducted in this paper.
Most of the tedious formatting requirements do not match what the final typeset article looks like. The requirements are instead theoretically to benefit peer reviewers, e.g., by having double-spaced lines so they can write their comments on the paper copy that was mailed to them back when the submission guidelines were written in the 1950s.
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.
Outside of disciplines that use LaTeX, the ability of authors to do typesetting is pretty limited. And there are other typesetting requirements that no consumer tool makes particularly easy; for instance, due to funding requirements, many journals deposit biomedical papers with PubMed Central, which wants them in JATS XML. So publishers have to prepare a structured XML version of papers.
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.
It didn't "survey" devs. It paid them to complete real tasks while they were randomly assigned to use AI or not, and measured the actual time taken to complete the tasks vs. just the perception. It is much higher quality evidence than a convenience sample of developers who just report their perceptions.
Sure, if you're learning to write and want lots of examples of a particular style, LLMs can generate that for you. Just don't assume that is a normal writing style, or that it matches a particular genre (say, workplace communication, or academic writing, or whatever).
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.
I don't think the AI companies are systematically working to make their models sound more human. They're working to make them better at specific tasks, but the writing styles are, if anything, even more strange as they advance.
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.
Probably because the article uses the Unicode right single quotation mark instead of apostrophes, due to some automated smart-quote machinery. I'll have to adjust the tagger to handle those.
In our studies of ChatGPT's grammatical style (https://arxiv.org/abs/2410.16107), it really loves past and present participial phrases (2-5x more usage than humans). I didn't see any here in a glance through the lightfastness section, though I didn't try running the whole article through spaCy to check. In any case it doesn't trip my mental ChatGPT detector either; it reads more like classic SEO writing you'd see all over blogs in the 20-teens.
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.
OpenAI is the custodian of the user data, so they are responsible. If you wanted the court (i.e., the plaintiffs) to find specific infringing chatters, first they'd have to get the data from OpenAI to find who it is -- which is exactly what they're trying to do, and why OpenAI is being told to preserve the data so they can review it.
If the output is interpreting sources rather than just regurgitating quotes from them, you need to exert judgment to verify they support its claims. When the LLM output is about some highly technical subject, it can require expert knowledge just to judge whether the source supports the claims.
Courts have always had the power to compel parties to a current case to preserve evidence. (For example, this was an issue in the Google monopoly case, since Google employees were using chats set to erase after 24 hours.) That becomes an issue in the discovery phase, well after the defendant has an opportunity to file a motion to dismiss. So a case with no specific allegation of wrongdoing would already be dismissed.
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.