🌐 LLMTrace Website | 📜 LLMTrace Paper on arXiv | 🤗 LLMTrace - Classification Dataset | Github |
## Model Card ### Model Description This is the official `GigaCheck-Classifier-Multi` model from the `LLMTrace` project. It is a multilingual transformer-based model trained for the **binary classification of text** as either `human` or `ai`. The model was trained jointly on the English and Russian portions of the `LLMTrace Classification dataset`. It is designed to be a robust baseline for detecting AI-generated content across multiple domains, text lengths and prompt types. For complete details on the training data, methodology, and evaluation, please refer to our research paper: link(coming soon) ### Intended Use & Limitations This model is intended for academic research, analysis of AI-generated content, and as a baseline for developing more advanced detection tools. **Limitations:** * The model's performance may degrade on text generated by LLMs released after its training date (September 2025). * It is not infallible and can produce false positives (flagging human text as AI) and false negatives. * Performance may vary on domains or styles of text not well-represented in the training data. ## Evaluation The model was evaluated on the test split of the `LLMTrace Classification dataset`, which was not seen during training. Performance metrics are reported below: | Metric | Value | |-----------------------|---------| | F1 Score (AI) | 98.64 | | F1 Score (Human) | 98.00 | | Mean Accuracy | 98.46 | | TPR @ FPR=0.01 | 97.93 | ## Citation If you use this model in your research, please cite our papers: ```bibtex @article{Layer2025LLMTrace, Title = {{LLMTrace: A Corpus for Classification and Fine-Grained Localization of AI-Written Text}}, Author = {Irina Tolstykh and Aleksandra Tsybina and Sergey Yakubson and Maksim Kuprashevich}, Year = {2025}, Eprint = {arXiv:2509.21269} } @article{tolstykh2024gigacheck, title={{GigaCheck: Detecting LLM-generated Content}}, author={Irina Tolstykh and Aleksandra Tsybina and Sergey Yakubson and Aleksandr Gordeev and Vladimir Dokholyan and Maksim Kuprashevich}, journal={arXiv preprint arXiv:2410.23728}, year={2024} } ```