roberta-large-openai-detector
RoBERTa large OpenAI Detector is the GPT-2 output detector model, obtained by fine-tuning a RoBERTa large model with the outputs of the 1.5B-parameter GPT-2 model. The model can be used to predict if text was generated by a GPT-2 model. This model was released by OpenAI at the same time as OpenAI released the weights of the largest GPT-2 model , the 1.5B parameter version.
The model is a classifier that can be used to detect text generated by GPT-2 models.
The model's developers have stated that they developed and released the model to help with research related to synthetic text generation, so the model could potentially be used for downstream tasks related to synthetic text generation. See the associated paper for further discussion.
Training Data
The model is a sequence classifier based on RoBERTa large (see the RoBERTa large model card for more details on the RoBERTa large training data) and then fine-tuned using the outputs of the 1.5B GPT-2 model (available here ).
Training Procedure
The model developers write that:
We based a sequence classifier on RoBERTaLARGE (355 million parameters) and fine-tuned it to classify the outputs from the 1.5B GPT-2 model versus WebText, the dataset we used to train the GPT-2 model.
They later state:
To develop a robust detector model that can accurately classify generated texts regardless of the sampling method, we performed an analysis of the model’s transfer performance.
See the associated paper for further details on the training procedure.
The following evaluation information is extracted from the associated paper .
The model is intended to be used for detecting text generated by GPT-2 models, so the model developers test the model on text datasets, measuring accuracy by:
testing 510-token test examples comprised of 5,000 samples from the WebText dataset and 5,000 samples generated by a GPT-2 model, which were not used during the training.
The model developers find :
Our classifier is able to detect 1.5 billion parameter GPT-2-generated text with approximately 95% accuracy...The model’s accuracy depends on sampling methods used when generating outputs, like temperature, Top-K, and nucleus sampling (Holtzman et al., 2019 . Nucleus sampling outputs proved most difficult to correctly classify, but a detector trained using nucleus sampling transfers well across other sampling methods. As seen in Figure 1 [in the paper], we found consistently high accuracy when trained on nucleus sampling.
See the associated paper , Figure 1 (on page 14) and Figure 2 (on page 16) for full results.
