Awesome
:detective: Detecting Pretraining Data from Large Language Models
This repository provides an original implementation of Detecting Pretraining Data from Large Language Models by *Weijia Shi, *Anirudh Ajith, Mengzhou Xia, Yangsibo Huang, Daogao Liu , Terra Blevins , Danqi Chen , Luke Zettlemoyer
Website | Paper | WikiMIA Benchmark | BookMIA Benchmark | Detection Method Min-K% Prob(see the following codebase)
Overview
We explore the pretraining data detection problem: given a piece of text and black-box access to an LLM without knowing the pretraining data, can we determine if the model was trained on the provided text? To faciliate the study, we built a dynamic benchmark WikiMIA to systematically evaluate detecting methods and proposed Min-K% Prob 🕵️, a method for detecting undisclosed pretraining data from large language models.
<p align="center"> <img src="mink_prob.png" width="80%" height="80%"> </p>:star: If you find our implementation and paper helpful, please consider citing our work :star: :
@misc{shi2023detecting,
title={Detecting Pretraining Data from Large Language Models},
author={Weijia Shi and Anirudh Ajith and Mengzhou Xia and Yangsibo Huang and Daogao Liu and Terra Blevins and Danqi Chen and Luke Zettlemoyer},
year={2023},
eprint={2310.16789},
archivePrefix={arXiv},
primaryClass={cs.CL}
}
📘 WikiMIA Datasets
The WikiMIA datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from extensive large language models. Access our WikiMIA datasets directly on Hugging Face.
Loading the Datasets:
from datasets import load_dataset
LENGTH = 64
dataset = load_dataset("swj0419/WikiMIA", split=f"WikiMIA_length{LENGTH}")
- Available Text Lengths:
32, 64, 128, 256
. - Label 0: Refers to the unseen data during pretraining. Label 1: Refers to the seen data.
- WikiMIA is applicable to all models released between 2017 to 2023 such as
LLaMA1/2, GPT-Neo, OPT, Pythia, text-davinci-001, text-davinci-002 ...
📘 BookMIA Datasets for evaluating MIA on OpenAI models
The BookMIA datasets serve as a benchmark designed to evaluate membership inference attack (MIA) methods, specifically in detecting pretraining data from OpenAI models that are released before 2023 (such as text-davinci-003). Access our BookMIA datasets directly on Hugging Face.
The dataset contains non-member and member data:
- non-member data consists of text excerpts from books first published in 2023
- member data includes text excerpts from older books, as categorized by Chang et al. in 2023.
Loading the Datasets:
from datasets import load_dataset
dataset = load_dataset("swj0419/BookMIA")
- Available Text Lengths:
512
. - Label 0: Refers to the unseen data during pretraining. Label 1: Refers to the seen data.
- WikiMIA is applicable to OpenAI models that are released before 2023
text-davinci-003, text-davinci-002 ...
🚀 Run our Min-K% Prob & Other Baselines
Our codebase supports many models: Whether you're using OpenAI models that offer logits or models from Huggingface, we've got you covered:
-
OpenAI Models:
text-davinci-003
text-davinci-002
- ...
-
Huggingface Models:
meta-llama/Llama-2-70b
huggyllama/llama-70b
EleutherAI/gpt-neox-20b
- ...
🔐 Important: When using OpenAI models, ensure to add your API key at Line 38
in run.py
:
openai.api_key = "YOUR_API_KEY"
Use the following command to run the model:
python src/run.py --target_model text-davinci-003 --ref_model huggyllama/llama-7b --data swj0419/WikiMIA --length 64
🔍 Parameters Explained:
-
Target Model: Set using --target_model. For instance, --target_model huggyllama/llama-70b.
-
Reference Model: Defined using --ref_model. Example: --ref_model huggyllama/llama-7b.
-
Data Length: Define the length for the WikiMIA benchmark with --length. Available options: 32, 54, 128, 256.
<span style="color:red;">📌 Note: For optimal results, use fixed-length inputs with our Min-K% Prob method</span> (When you evalaute Min-K% Prob method on your own dataset, make sure the input length of each example is the same.)
📊 Baselines: Our script comes with the following baselines: PPL, Calibration Method, PPL/zlib_compression, PPL/lowercase_ppl