xlmr-chatgptdetect-noisy

Multilingual ChatGPT detection model from Towards a Robust Detection of Language Model-Generated Text: Is ChatGPT that easy to detect?

This model is a fine-tuned version of xlm-roberta-base on the HC3 FULL_MULTI_1.0_0.5_0.5 dataset with noise added. It achieves the following results on the:

Evaluation set:

• Loss: 0.1573
• F1: 0.9633

Test Set:

• F1: 0.97

Adversarial:

• F1: 0.45

Model description

This a model trained to detect text created by ChatGPT in French. The training data is the combination of the hc3_fr_full and hc3_en_full subsets of almanach/hc3_multi, but with added misspelling and homoglyph attacks.

Intended uses & limitations

This model is for research purposes only. It is not intended to be used in production as we said in our paper:

We would like to emphasize that our study does not claim to have produced an universally accurate detector. Our strong results are based on in-domain testing and, unsurprisingly, do not generalize in out-of-domain scenarios. This is even more so when used on text specifically designed to fool language model detectors and on text intentionally stylistically similar to ChatGPT-generated text, especially instructional text.

Training procedure

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 2e-05
• train_batch_size: 8
• eval_batch_size: 8
• seed: 1
• gradient_accumulation_steps: 4
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 5.0
• mixed_precision_training: Native AMP

Training results

Framework versions

• Transformers 4.26.1
• Pytorch 1.11.0+cu115
• Datasets 2.8.0
• Tokenizers 0.13.2