Skip to content

Latest commit

 

History

History
130 lines (105 loc) · 3.47 KB

README.md

File metadata and controls

130 lines (105 loc) · 3.47 KB
Raw

POGER

Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling

Preprint

Paper

Chinese Blog

Requirements

  • Python: 3.11
  • CUDA: 11.8
  • Python Packages: 
    pip install -r requirements.txt

Datasets

The binary, multiclass and OOD AIGT datasets are available at Google Drive.

Run

1. Preprocess

This step is optional, as processed POGER Features and POGER-Mixture Features can be downloaded at Google Drive.

Obtain POGER Features

cd get_feature
export HF_TOKEN=hf_xxx        # Fill in your HuggingFace access token
export OPENAI_API_KEY=sk-xxx  # Fill in your OpenAI API key

python get_poger_feature.py \
    --n 100 \
    --k 10 \
    --delta 1.2 \
    --input ../data/train.jsonl \
    --output ./train_poger_feature.jsonl
python get_poger_feature.py \
    --n 100 \
    --k 10 \
    --delta 1.2 \
    --input ../data/test.jsonl \
    --output ./test_poger_feature.jsonl

Obtain POGER-Mixture Features

Inference on white-box LLMs

This part of the code is modified from Jihuai-wpy/SeqXGPT under the Apache License 2.0.

cd get_feature/get_true_prob

# Launch inference server
nohup python backend_api.py --model gpt2 --gpu 0 --port 6001 &
nohup python backend_api.py --model gptj --gpu 0 --port 6002 &
nohup python backend_api.py --model llama2 --gpu 1 --port 6003 &
nohup python backend_api.py --model alpaca --gpu 2 --port 6004 &
nohup python backend_api.py --model vicuna --gpu 3 --port 6005 &

# Get true probability
python get_true_prob.py
Mixing true probability and estimated probability
cd get_feature

python get_poger_mix_feature.py \
    --poger-feature ./train_poger_feature.jsonl \
    --true-prob ./get_true_prob/result/train_true_prob.jsonl \
    --output ./train_poger_mix_feature.jsonl
python get_poger_mix_feature.py \
    --poger-feature ./test_poger_feature.jsonl \
    --true-prob ./get_true_prob/result/test_true_prob.jsonl \
    --output ./test_poger_mix_feature.jsonl

2. Train

cd POGER

# POGER
python main.py \
    --cuda \
    --model poger \
    --data-dir ../get_feature \
    --data-name full_data

## POGER-Mixture
python main.py \
    --cuda \
    --model poger_mix \
    --data-dir ../get_feature \
    --data-name full_data

3. Test

cd POGER

# POGER
python main.py \
    --cuda \
    --model poger \
    --data-dir ../get_feature \
    --test ./params/params_poger_full_data.pt

# POGER-Mixture
python main.py \
    --cuda \
    --model poger_mix \
    --data-dir ../get_feature \
    --test ./params/params_poger_mix_full_data.pt

How to Cite

@inproceedings{shi2024ten,
  title={{Ten Words Only Still Help: Improving Black-Box AI-Generated Text Detection via Proxy-Guided Efficient Re-Sampling}},
  author={Shi, Yuhui and Sheng, Qiang and Cao, Juan and Mi, Hao and Hu, Beizhe and Wang, Danding},
  booktitle={Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence},
  pages={494--502},
  doi={10.24963/ijcai.2024/55},
  year={2024}
}