4 ms·

Nothing points out that the benchmark is invalid like a zero false positive rate. Seemingly it is pre-2020 text vs a few models rework of texts. I can see this

by nialse 11mo ago

Nothing points out that the benchmark is invalid like a zero false positive rate. Seemingly it is pre-2020 text vs a few models rework of texts. I can see this model fall apart in many real world scenarios. Yes, LLMs use strange language if left to their own devices and this can surely be detected. 0% false positive rate under all circumstances? Implausible.

maxspero 11mo ago
Our benchmarks of public datasets put our FPR roughly around 1 in 10,000. https://www.pangram.com/blog/all-about-false-positives-in-ai-detectors https://www.pangram.com/blog/all-about-false-positives-in-ai... Find me a clean public dataset with no AI involvement and I will be happy to report Pangram's false positive rate on it.
- Oarch 11mo ago
  I enjoyed this thoughtful write up. It's a vitally important area for good, transparent work to be done.
- Grimblewald 11mo ago
  [flagged]
- godelski 11mo ago
  Max, there's two problems I see with your comment. 1) the paper didn't show a 0% FNR. I mean tables 4, 7, and B.2 are pretty explicit. It's not hard to figure out from the others either. 2) a 0% error rate requires some pretty serious assumptions to be true. For that type of result to not be incredibly suspect requires there to be zero noise in the data, analysis, and at all parts. I do not see that being true of the mentioned dataset. Even high scores are suspect. Generalizing the previous a score is suspect if it is higher than the noise level. Can you truly attest that this condition is true? I'm suspect that you're introducing data leakage. I haven't looked enough into your training and data to determine how that's happening but you'll probably need a pretty deep analysis as leakage is really easy to sneak in. It can do so in non obvious ways. A very common one is tuning hyper parameters on test results. You don't have to pass data to pass information. Another sly way for this to happen is that the test set isn't significantly disjoint from the training set. If the perturbation is too small then you aren't testing generalization you're testing a slightly noisy training set (which your training should be introducing noise to help regularize, so you end up just measuring your training performance). Your numbers are too good and that's suspect. You need a lot more evidence to suggest they mean what you want them to mean.
pinkmuffinere 11mo ago
> Nothing points out that the benchmark is invalid like a zero false positive rate You’re punishing them for claiming to do a good job. If they truly are doing a bad job, surely there is a better criticism you could provide.
- deleted 11mo ago
  [deleted]
- nialse 11mo ago
  No one is punishing anyone. They just make an implausible claim. That is it.

by nialse 11mo ago

maxspero 11mo ago

Our benchmarks of public datasets put our FPR roughly around 1 in 10,000. https://www.pangram.com/blog/all-about-false-positives-in-ai-detectors https://www.pangram.com/blog/all-about-false-positives-in-ai... Find me a clean public dataset with no AI involvement and I will be happy to report Pangram's false positive rate on it.

Oarch 11mo ago
I enjoyed this thoughtful write up. It's a vitally important area for good, transparent work to be done.
Grimblewald 11mo ago
[flagged]
godelski 11mo ago
Max, there's two problems I see with your comment. 1) the paper didn't show a 0% FNR. I mean tables 4, 7, and B.2 are pretty explicit. It's not hard to figure out from the others either. 2) a 0% error rate requires some pretty serious assumptions to be true. For that type of result to not be incredibly suspect requires there to be zero noise in the data, analysis, and at all parts. I do not see that being true of the mentioned dataset. Even high scores are suspect. Generalizing the previous a score is suspect if it is higher than the noise level. Can you truly attest that this condition is true? I'm suspect that you're introducing data leakage. I haven't looked enough into your training and data to determine how that's happening but you'll probably need a pretty deep analysis as leakage is really easy to sneak in. It can do so in non obvious ways. A very common one is tuning hyper parameters on test results. You don't have to pass data to pass information. Another sly way for this to happen is that the test set isn't significantly disjoint from the training set. If the perturbation is too small then you aren't testing generalization you're testing a slightly noisy training set (which your training should be introducing noise to help regularize, so you end up just measuring your training performance). Your numbers are too good and that's suspect. You need a lot more evidence to suggest they mean what you want them to mean.

pinkmuffinere 11mo ago

> Nothing points out that the benchmark is invalid like a zero false positive rate You’re punishing them for claiming to do a good job. If they truly are doing a bad job, surely there is a better criticism you could provide.

deleted 11mo ago
[deleted]
nialse 11mo ago
No one is punishing anyone. They just make an implausible claim. That is it.