chenhongqiao/Evade-GPT-Detector

Source code for paper **Large Language Models can be Guided to Evade AI-Generated Text Detection**

Large Language Models can be Guided to Evade AI-Generated Text Detection

Source code for paper Large Language Models can be Guided to Evade AI-Generated Text Detection.

Introduction

We introduce SICO, a Substitution-based In-Context example Optimization method, which automatically build prompt that guide Large Language Models (LLMs), such as ChatGPT, to generate human-like texts.

SICO successfully evades all exisiting AI-generation text detectors, including GPTzero and OpenAI official detector.

Run SICO

Requirements

Create environment by conda:

conda env create -f environment.yml

LLM setup

• For ChatGPT, you need to set OpenAI API keys in the environment export OPENAI_API_KEY={your OpenAI API key}.
• For Vicuna, please follow the instruction here. And set the local url in sico/LLM_api.py.

Detectors setup

• GPTzero: Set the GPTzero API key in environment export GPTZERO_API_KEY={your GPTzero API key}. Key can be obtained from GPTzero website.
• OpenAI detector: Set the OpenAI API in environment export OPENAI_API_KEY={your OpenAI API key}
• ChatGPT detector, GPT2 detector, DetectGPT, Log-Rank: Required models will be automatically download model from HuggingFace.

Datasets

All datasets we used are listed in datasets folder, containing squad,eli5,yelp. Each subfolder has three tsv files: eval.tsv for evaluation during training, test.tsv for final test, and incontext.tsv for initialization and building in-context examples.

Run training

Run SICO_train.py to start training procedure.

Here we explain each required argument in details:

• --llm: Base LLM used for training, including [chatgpt, vicuna]
• --dataset: Dataset we use, including [squad, eli5, yelp]
• --detector: Proxy detector we use for training, including ['chatdetect', 'gpt2detect', 'gptzero', 'openai', 'detectgpt', 'logrank']
• --task: The task type of training, including ['essay', 'qa', 'rev-gen', 'paraphrase']. Notice that paraphrase task matches all dataset, but essay, qa, rev-gen tasks only match squad, eli5, yelp, respectively.
• --incontext-size: Size of in-context examples.
• --eval-size: Size of evaluation data during training.
• --train-iter: Maximum training iteration.

Examples:

Reimplement of SICO-gen on essay writing task of SQuAD dataset:

python SICO_train.py 
    --dataset squad 
    --llm chatgpt 
    --detector chatdetect 
    --task essay
    --incontext-size 8
    --eval-size 32
    --train-iter 6

Reimplement of SICO-para for open-ended question answering task:

python SICO_train.py 
    --dataset yelp 
    --llm chatgpt 
    --detector chatdetect 
    --task paraphrase
    --incontext-size 8
    --eval-size 32
    --train-iter 6

After training, the optimized prompt is stored in ./outputs/results/ and training log is stored in ./outputs/logs/.

Run testing

Run SICO_test_gen.py to use trained-prompt to generate texts, the arguments are the same as SICO_train.py. Extra parameter --test-size show the number of cases you want to test.

After running SICO_test_gen.py, the generated texts are stored in test_results/{dataset}/SICO-{dataset}-{task}-{llm}-{proxy_detector} folder. Then run run_test_detection.py to get the AI-generated probability from different detectors, which is stored in same folder, named {test_detector}_score.tsv.

Examples:

Test SICO-gen on essay writing task of SQuAD dataset, where the prompt is trained using ChatGPT and ChatGPT Detector:

python SICO_test_gen.py 
    --dataset squad 
    --llm chatgpt 
    --detector chatdetect 
    --task essay
    --incontext-size 8
    --eval-size 32
    --train-iter 6
    --test-size 100

Test the performance of optimized prompt against DetectGPT detector:

python run_test_detection.py 
    --dataset squad
    --method SICO-squad-essay-chatgpt-chatdetect 
    --detector detectgpt

To-Do List

Here is our planned roadmap for future developments:

• Open Source Code
• Share Effective Prompts
• Share Benchmark

Stay tuned for further updates and developments on these tasks. We encourage community engagement and welcome any form of feedback or contributions to our project.