One of AI’s strengths is definitely exploration, f.e. in finding bugs, but it still has a high false positive rate. Depending on context that matters or it wont.
Also one has to be aware that there are a lot of bugs that AI won’t find but humans would
I don’t have the expertise to verify this bug actually happened, but I’m curious.
I don't think Pangram reliably detects individual LLM-generated phrases. It seems to look at sections of ~300 words. And for one section at least it has low confidence.
It might be tuned for ChatGPT and not work well for Claude Opus 4.6 as well.
Not sure how I feel about the whole "LLMs learned from human texts, so now the people who helped write human texts are suddenly accused of plagiarizing LLMs" thing yet, but seems backwards so far and like a low quality criticism.
Is it possible for a tool to know if something is AI written with high confidence at all? LLMs can be tuned/instructed to write in an infinite number of styles.
They found that Pangram suffers from false positives in non-prose contexts like bibliographies, outlines, formatting, etc. The article does not touch on Pangram’s false negatives.
I personally think it’s an intractable problem, but I do feel pangram gives some useful signal, albeit not reliably.
Any specific sections that stick out? Juxt in the past had really great articles, even before LLMs, and know for a fact they don't lack the expertise or knowledge to write for themselves if they wanted and while I haven't completely read this article yet, I'd surprise me if they just let LLMs write articles for them today.
That's just writing. I frequently write like that.
This insistence that certain stylistics patterns are "tell-tale" signs that an article was written by AI makes no sense, particularly when you consider that whatever stylistic ticks an LLM may possess are a result of it being trained on human writing.
I am reminded of the Simpsons episode in which Principal Skinner tries to pass off the hamburgers from a near-by fast food restaurant for an old family recipe, 'steamed hams,' and his guest's probing into the kitchen mishaps is met with increasingly incredible explanations.
My hunch that this is substantially LLM-generated is based on more than that.
In my head it's like a Bayesian classifier, you look at all the sentences and judge whether each is more or less likely to be LLM vs human generated. Then you add prior information like that the author did the research using Claude - which increases the likelihood that they also use Claude for writing.
Maybe your detector just isn't so sensitive (yet) or maybe I'm wrong but I have pretty high confidence at least 10% of sentences were LLM-generated.
Yes, the stylistic patterns exist in human speech but RLHF has increased their frequency. Also, LLM writing has a certain monotonicity that human writing often lacks. Which is not surprising: the machine generates more or less the most likely text in an algorithmic manner. Humans don't. They wrote a few sentences, then get a coffee, sleep, write a few more. That creates more variety than an LLM can.
Here's an alternative way of thinking about this...
Someone probably expended a lot of time and effort planning, thinking about, and writing an interesting article, and then you stroll by and casually accuse them of being a bone idle cheat, with no supporting evidence other than your "sensitive detector" and a bunch of hand-wavy nonsense that adds up to naught.
In theory, wouldn't be too hard be to settle the question if whether he used ChatGPT to write it: get Olang to write a few paragraphs by hand, then have people judge (blindly) if it's the same style as the article. Which one sounds more like ChatGPT.
The times I've written articles, and those have gone through multiple rounds of reviews (by humans) with countless edits each time, before it ends up being published, I wonder if I'd pass that test in those cases. Initial drafts with my scattered thoughts usually are very different from the published end results, even without involving multiple reviewers and editors.
I have nothing against em dashes. As long as your writing is human, experienced readers will be able to tell it's human. Only less experienced ones will use all or nothing rules. Em dashes just increase the likelihood that the text was LLM generated. They aren't proof.
I'm starting to develop a physiological response when I recognize AI prose. Just like an overwhelming frustration, as if I'm hearing nails on chalkboard silently inside of my head.
I feel ya.... and i have to admit in the past i tried it for one article in my own blog thinking it might help me to express... tho when i read that post now i dont even like it myself its just not my tone.
therefor decided not gonne use any llm for blogging again and even tho it takes alot more time without (im not a very motivated writer) i prefer to release something that i did rather some llm stuff that i wouldnt read myself.
This is so insightfully and powerfully written I had literal chills running down my spine by the end.
What a horrible world we live in where the author of great writing like this has to sit and be accused of "being AI slop" simply because they use grammar and rhetoric well.
I was completely riveted the whole read. The description of Collins' dilemma is the first time I've seen an actual real world scenario described that might cause him to return to Earth alone.
If an LLM wrote that, then I no longer oppose LLM art.
One of AI’s strengths is definitely exploration, f.e. in finding bugs, but it still has a high false positive rate. Depending on context that matters or it wont.
Also one has to be aware that there are a lot of bugs that AI won’t find but humans would
I don’t have the expertise to verify this bug actually happened, but I’m curious.
It might be tuned for ChatGPT and not work well for Claude Opus 4.6 as well.
Not sure how I feel about the whole "LLMs learned from human texts, so now the people who helped write human texts are suddenly accused of plagiarizing LLMs" thing yet, but seems backwards so far and like a low quality criticism.
Don't understand how these tools exist.
They found that Pangram suffers from false positives in non-prose contexts like bibliographies, outlines, formatting, etc. The article does not touch on Pangram’s false negatives.
I personally think it’s an intractable problem, but I do feel pangram gives some useful signal, albeit not reliably.
What's making it even more difficult to tell now is people who use AI a lot seem to be actively picking up some of its vocab and writing style quirks.
It seems like almost every discussion has at least someone complaining about "AI slop" in either the original post or the comments.
Another one: "Two instructions are missing: [...] Four bytes."
One more: "The defensive coding hid the problem, but it didn’t eliminate it."
This insistence that certain stylistics patterns are "tell-tale" signs that an article was written by AI makes no sense, particularly when you consider that whatever stylistic ticks an LLM may possess are a result of it being trained on human writing.
My hunch that this is substantially LLM-generated is based on more than that.
In my head it's like a Bayesian classifier, you look at all the sentences and judge whether each is more or less likely to be LLM vs human generated. Then you add prior information like that the author did the research using Claude - which increases the likelihood that they also use Claude for writing.
Maybe your detector just isn't so sensitive (yet) or maybe I'm wrong but I have pretty high confidence at least 10% of sentences were LLM-generated.
Yes, the stylistic patterns exist in human speech but RLHF has increased their frequency. Also, LLM writing has a certain monotonicity that human writing often lacks. Which is not surprising: the machine generates more or less the most likely text in an algorithmic manner. Humans don't. They wrote a few sentences, then get a coffee, sleep, write a few more. That creates more variety than an LLM can.
Fun exercise: https://en.wikipedia.org/wiki/Wikipedia:AI_or_not_quiz
Someone probably expended a lot of time and effort planning, thinking about, and writing an interesting article, and then you stroll by and casually accuse them of being a bone idle cheat, with no supporting evidence other than your "sensitive detector" and a bunch of hand-wavy nonsense that adds up to naught.
For what it’s worth, Pangram reports that Marcus’ article is 100% LLM-written: https://www.pangram.com/history/640288b9-e16b-4f76-a730-8000...
Even though they are perfect for usage in writing down thoughts and notes.
In fact, the latter is the opposite of terseness. LLMs love to tell you what things are not way more than people do.
See https://www.blakestockton.com/dont-write-like-ai-1-101-negat...
(The irony that I started with "it's not just" isn't lost on me)
It is:
- sneering
- a shallow dismissal (please address the content)
- curmudgeonly
- a tangential annoyance
All things explicitly discouraged in the site guidelines. [1]
Downvoting is the tool for items that you think don't belong on the front page. We don't need the same comment on every single article.
[1] - https://news.ycombinator.com/newsguidelines.html
You can’t downvote submissions. That’s literally not a feature of the site. You can only flag submissions, if you have more that 31 karma.
The short sentence construction is the most suspicious, but I actually don't see anything glaring. It normally jumps out and hits me in the face.
therefor decided not gonne use any llm for blogging again and even tho it takes alot more time without (im not a very motivated writer) i prefer to release something that i did rather some llm stuff that i wouldnt read myself.
What a horrible world we live in where the author of great writing like this has to sit and be accused of "being AI slop" simply because they use grammar and rhetoric well.
If an LLM wrote that, then I no longer oppose LLM art.