91–100 of 184 posts
Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it. Sure you might be able to detect today's tells (particular sentence structures preferred by Claude, phrases, etc) to get you some arbitrary chance percentage it was machine generated, but it's a bad fiction to perpetuate that any of this is anything more than tarot card reading. Images, absolutely, there are…
Earlier quoted context omitted.
So, if the decision from Pangram determined, on every assignment, if you would be expelled from university for plagiarism, would that be acceptable to you regardless of how you actually did the work? If you would not be okay with that, what level of consequence would be acceptable for the output from this tool?
That’s a different point. I’d want detectors to be as accurate as possible, false positives of 1 in 10000 seems like a good starting point. I believe their results have been independently tested. And as a separate matter, any tool for evaluating students should be applied fairly, safely, and with adequate human review and due process. You need good tools and good oversight.
Agreed, that's a fair and reasonable stance.
The reason I asked is that I have a hard time understanding the point of these tools. When it comes to education, it can be a matter of learning objectives. But outside that, what's the point?
The prediction from the tool is pointless for deciding on copyright or contract issues, and other text should be judged on its correctness or applicability to the task.
If all the tool is good for is "maybe this student cheated, but only an in-depth investigation would maybe prove it", it isn't a very useful tool, because it's more straightforward to just mandate that evidence is submitted regardless of what the tool says. On top of that, even the lack of evidence of manual work isn't good proof of using LLMs.
I think figuring out if a text is AI-made is a losing battle. What could work is gauging how much effort went into writing the text, regardless of who the author might be. What's easy today is generating mountains of text that are extremely hard to read. What requires effort is knowing how to engage the reader, how to keep out extraneous information, and how to keep the text as short as possible without losing detail…
The easiest way is to keep track of the text's edit history, keeping a block of edits over time and having them signed by a timestamp authority. The final edit history can then be inspected by some external authority, then signed if the edit history looks human. I have a blog post from 2023 on this topic: https://helbl.ing/Written-Proof-of-Work/ For Google Doc users, you can already inspect the edit history over time…
I wish there was a solution
Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it. Sure you might be able to detect today's tells (particular sentence structures preferred by Claude, phrases, etc) to get you some arbitrary chance percentage it was machine generated, but it's a bad fiction to perpetuate that any of this is anything more than tarot card reading. Images, absolutely, there are…
Most people's issue with AI-generated llmish however is not that it's AI-generated. It's its insufferable tone.
So if we get to a point where we have to read tea leaves (an image you seem to appreciate) to determine if it's llmish or not, we'll have won by then.
Really: it's that full-on asshole tone I (and many others) want to see disappear from blogs, comments, LinkedIn, etc.
Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it. Sure you might be able to detect today's tells (particular sentence structures preferred by Claude, phrases, etc) to get you some arbitrary chance percentage it was machine generated, but it's a bad fiction to perpetuate that any of this is anything more than tarot card reading. Images, absolutely, there are…
Whether a text was written by a human or not is just a single bit of information. So you can't rule out its detectability a priori, since even the shortest text contains more information than that. As long as LLMs are used to write texts humans wouldn't want to write if they could help it (that's why they're getting an LLM to do it, after all), they'll remain detectable. Even if the reasoning might end up equivalent…
I doubt this models reality well at all. If I write the first paragraph, and AI writes the second; a float seems to model that better. If you choose to collapse a float into a bool, I don't think you can make useful conclusions based on that bit?
> since even the shortest text contains more information than that.
I also don't think that's how information theory and bits of information works...
Earlier quoted context omitted.
"Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it...it's a bad fiction to perpetuate that any of this is anything more than tarot card reading." Not true at all. Pangram is highly effective and has a very low false positive rate. The post here is impressive for a small project, it looks like they independently thought of one of the core ideas Pangram uses of…
So, if the decision from Pangram determined, on every assignment, if you would be expelled from university for plagiarism, would that be acceptable to you regardless of how you actually did the work? If you would not be okay with that, what level of consequence would be acceptable for the output from this tool?
FWIW if I were a student I would definitely be using Track Changes or version control, etc etc, to make clear my work was human-written. Which sucks.
Earlier quoted context omitted.
I am working on a browser extension to help with that. Basically it interposes on any text field and canvas and if user pastes a large amount of text (copied form example from a chat bot), the extension will "replay" that text at normal, human-editing pace, and introduce typos that are fixed through later edits.
Any specific reason as to why you'd want to make that, outside of intentionally enabling fraud?
Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it. Sure you might be able to detect today's tells (particular sentence structures preferred by Claude, phrases, etc) to get you some arbitrary chance percentage it was machine generated, but it's a bad fiction to perpetuate that any of this is anything more than tarot card reading. Images, absolutely, there are…
Schemes like GPS, CDMA and DSSS are based upon this concept. GPS in particular is quite impressive in its ability to recover information that is received below the thermal noise floor.
I think the fundamental problem is that training current SOTA AI models is very expensive. If a simple "classical" model can detect them, presumably at much lower algorithmic cost, then why wouldn't the model trainers use these same tools to feed back into their models to improve them at low cost to make them better? It's an arms race. Any cheap pattern can and presumably will be used to retrain if it becomes and eff…
Text is simply not information dense enough to be able to decode some arbitrary signal of provenance from it. Sure you might be able to detect today's tells (particular sentence structures preferred by Claude, phrases, etc) to get you some arbitrary chance percentage it was machine generated, but it's a bad fiction to perpetuate that any of this is anything more than tarot card reading. Images, absolutely, there are…
Whether a text was written by a human or not is just a single bit of information. So you can't rule out its detectability a priori, since even the shortest text contains more information than that. As long as LLMs are used to write texts humans wouldn't want to write if they could help it (that's why they're getting an LLM to do it, after all), they'll remain detectable. Even if the reasoning might end up equivalent…
Come on, that's circular reasoning.