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

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?

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.

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 [email protected], 0.971 [email protected]

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?

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.

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.