ReMoDetect

Official PyTorch implementation of "ReMoDetect: Reward Models Recognize Aligned LLM's Generations".

Prepare Data

• Download the cleaned full-text HC3 dataset in English from YuchuanTian/AIGC_text_detector.git.
• Download the unfilter_full/en_train_cleaned.csv file from the repository linked above and extract it into the ./data directory.

Train and Evaluation Instructions

1. Configure Environment Variables

   Set your Groq API key in the data_process.sh script.

   export GROQ_API_KEY="your_groq_api_key_here"

2. Run Data Processing Script

   Execute the data_process.sh script to process the dataset.

   bash data_process.sh

3. Train

   Execute the train.sh script to process the dataset.

   bash train.sh

   Or, you can get trained weight from huggingface

4. Generate Evaluation Data (Optional)

   If you want to generate additional evaluation data, place your Azure, Anthropic, and Groq API keys in the gen_eval_data.sh script.

   export AZURE_API_KEY="your_azure_api_key_here"
   export ANTHROPIC_API_KEY="your_anthropic_api_key_here"
   export GROQ_API_KEY="your_groq_api_key_here"

   Then, run the script:

   bash gen_eval_data.sh

5. Evaluate

   Finally, run the eval.sh script to evaluate the model.

   bash eval.sh

   The benchmark based on the Fast-DetectGPT project and some codes include their licences.

Citation

@inproceedings{lee2024remodetect,
  title={ReMoDetect: Reward Models Recognize Aligned LLM's Generations},
  author={Lee, Hyunseok and Tack, Jihoon and Shin, Jinwoo},
  booktitle={Advances in Neural Information Processing Systems},
  year={2024}
}