My LoRA Fine-Tuned AI-generated Detector
This is a e5-small model fine-tuned with LoRA for sequence classification tasks. It is optimized to classify text into AI-generated or human-written with high accuracy.
- Label_0: Represents human-written content.
- Label_1: Represents AI-generated content.
Model Details
- Base Model:
intfloat/e5-small - Fine-Tuning Technique: LoRA (Low-Rank Adaptation)
- Task: Sequence Classification
- Use Cases: Text classification for AI-generated detection.
- Hyperparameters:
- Learning rate:
5e-5 - Epochs:
3 - LoRA rank:
8 - LoRA alpha:
16
- Learning rate:
Training Details
- Dataset:
- 10,000 twitters and 10,000 rewritten twitters with GPT-4o-mini.
- 80,000 human-written text from RAID-train.
- 128,000 AI-generated text from RAID-train.
- Hardware: Fine-tuned on a single NVIDIA A100 GPU.
- Training Time: Approximately 2 hours.
- Evaluation Metrics: | Metric | (Raw) E5-small | Fine-tuned | |--------|---------------:|-----------:| |Accuracy| 65.2% | 89.0% | |F1 Score| 0.653 | 0.887 | | AUC | 0.697 | 0.976 |
Collaborators
- Menglin Zhou
- Jiaping Liu
- Xiaotian Zhan
Citation
If you use this model, please cite the RAID dataset as follows:
@inproceedings{dugan-etal-2024-raid,
title = "{RAID}: A Shared Benchmark for Robust Evaluation of Machine-Generated Text Detectors",
author = "Dugan, Liam and
Hwang, Alyssa and
Trhl{\'\i}k, Filip and
Zhu, Andrew and
Ludan, Josh Magnus and
Xu, Hainiu and
Ippolito, Daphne and
Callison-Burch, Chris",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.674",
pages = "12463--12492",
}