[Krssst]

The classifier does not seem so big, I wonder if something like it for English could be used in a browser extension to run against every single paragraph being displayed ?

If the internet is going to drown in LLM text it would be nice to have tools to detect that automatically just like we have adblockers today to avoid wasting time on ads.

(the article was a good read, thanks!)

[cygn]

I built a browser extension that does this, well for posts on twitter, hackernews, reddit etc. If you want it for all text, it would also be feasible. I use a quantized mini-LM model that runs very fast and classifies eg your whole twitter feed in a couple of seconds.

Check it out: https://slopsieve.com/extension

Accuracy is also much higher than this approach here. 0.9944 AUC, 0.966 acc@.5, 0.971 F1@.5

[Anoian]

Went to the website and inserted the blogposts I wrote in the last year. I had a pretty good understanding which of my blogposts had more reworks and which ones had entire passages being generated using AI and then left as is because I was happy with them. None of my articles were over 50% according to your model but the ones that I know took me a long time to write, even though I used lots of AI in the creation of them, hit below 10%, probably because I hand-edited them a lot. Overall a nice website, thanks for sharing :)

[lcnhk42]

Hey this is really cool. Do you mind sharing a little bit about your approach? Is it similar SVM like algorithm or something else entirely?

[joouha]

Not GP, but I too was interested in this slopdetect-minilm-v3 model.

The model appears to be similar to MiniLM-L6 (384 dimension, 6 transformer layers) but uses RoBERTa/GPT-2 style embedding/tokenization (50265 vocab size), used for binary classification, and quantized to INT8.

My guess is that is was distilled from roberta-base, then fine-tuned on freely available pile like artem9k/ai-text-detection-pile or similar.

It's a nice model - I've just used it to create a browser extension which highlights text based on how likely it is to be LLM genereated.

Edit - a quick google search reveals ibm-granite/granite-embedding-30m-english with the same architecture: slap a binary classification head on, fine tune, job done.

[joouha]

Just noticed in the model's metadata:

> MiniLMv2 L6-H384 (30M params) progressively distilled L24→L12→L6 from RoBERTa-large-v8 teacher (neobert-v2 recipe, min_words=0). Byte-level BPE tokenizer. INT8 quantized.

[xiaoyu2006]

I assume different models will have different distribution, so it has to be kept updated?

[Krssst]

The article mentions that AI texts are often caught by multiple models, so hopefully text from newer LLMs could still be caught without updating the model?

[pixl97]

You know what GAN is, right?

In training all you have to do is take their model as the adversary and then it's useless.