[teeray]

The problems are simply too great if an LLM detector has any false positives at all. Imagine how soul-crushing writing an entire dissertation by hand and having it rejected because some “good enough” LLM detector decides you write too much like an AI.

[rayval]

As I recall, a few years ago (in the era of first generation LLMs), a professor in Texas used an anti-plagiarism tool that flagged more than one-third of the class using AI in an exam, and used that finding to give them a failing grade.

If memory serves, one student objected strenously and ran the professor's own work (published 10 years earlier) into the same tool and it flagged that work as AI-generated.

EDIT: HN item from June 2023 https://news.ycombinator.com/item?id=36215823

[pixl97]

Exactly. The more corporate and proper you tend to speak, the more likely it's to classify you as an LLM. It's like the classifiers want us to talk like trash at their current rate. This seems to be really problematic for ESL speakers/typers that may have been trained on a smaller, more proper subset of the language.

[dmurvihill]

It depends on the application. Dissertation? Hell naw. Blog post? Absolutely, run it through that thing.

[teeray]

The problem is that ed-tech is absolutely ravenous for an LLM detector and would rather use snake oil than accept that it might not be possible.

[Krssst]

We can measure false positive rate. The detector in the arricle is 85% accurate (not sure about false positives, but let's assume) which is too low to make conclusions, but enough when browsing the web and skipping reading likely-slop withiut accusing anyone.

If the false positive rate becomes <1% then it's better. The alternative is the world drowning under slop so I'd rather have imperfect detectors and have users aware they may fail in rare cases to avoid witch hunts. The general issue is that people only realize they're reading slop halfway through which is frustrating. If you know it from the start thanks to a detector and move on without commenting, no time waste, no frustration, less negativity towards LLM users.