Inherent Challenges of Post-Hoc Membership Inference for Large Language Models
Paper • 2406.17975 • Published
meta dict | text stringlengths 1 1.2M |
|---|---|
{
"arxiv_id": "2302.08637",
"language": "en",
"timestamp": "2023-02-20T02:04:29",
"url": "https://arxiv.org/abs/2302.08637",
"yymm": "2302"
} | \section{Introduction}\label{sec:introduction}
\IEEEPARstart{T}{raining} a high-performing deep learning model is incredibly costly, which consumes a massive amount of computational resources along with electricity, human resources, etc.
In fact, training such models is so time-consuming and expensive, that stealing a... |
{
"arxiv_id": "2302.08670",
"language": "en",
"timestamp": "2023-02-20T02:05:59",
"url": "https://arxiv.org/abs/2302.08670",
"yymm": "2302"
} | \section{}
Multispectral pedestrian detection is a technology designed to detect and locate pedestrians in Color and Thermal images, which has been widely used in automatic driving, video surveillance, etc. So far most available multispectral pedestrian detection algorithms only achieved limited success in pedestrian... |
{"arxiv_id":"2302.08582","language":"en","timestamp":"2023-02-20T02:02:07","url":"https://arxiv.org/(...TRUNCATED) | "\\section{Introduction}\\label{sec:intro}\n\n\\begin{figure}\n \\centering\n \n \\begin{sub(...TRUNCATED) |
{"arxiv_id":"2302.08606","language":"en","timestamp":"2023-02-20T02:03:02","url":"https://arxiv.org/(...TRUNCATED) | "\\section{Introduction}\nThe last two decades have witnessed an explosive development in deep learn(...TRUNCATED) |
{"arxiv_id":"2302.08682","language":"en","timestamp":"2023-02-20T02:06:28","url":"https://arxiv.org/(...TRUNCATED) | "\\section{Introduction}\nConvolutional Neural Network (CNN) is an important component in computer v(...TRUNCATED) |
{"arxiv_id":"2302.08681","language":"en","timestamp":"2023-02-20T02:06:27","url":"https://arxiv.org/(...TRUNCATED) | "\n\n\n\n\n\n\n\n\n\n\n\\section{Introduction}\n\\label{sec:intro}\n\\input{sections/introduction.te(...TRUNCATED) |
{"arxiv_id":"2302.08643","language":"en","timestamp":"2023-02-28T02:05:57","url":"https://arxiv.org/(...TRUNCATED) | "\n\\section{Introduction} \\label{sec:intro}\n\nTime series modeling has been a quest in a wide ran(...TRUNCATED) |
{"arxiv_id":"2302.08655","language":"en","timestamp":"2023-02-20T02:05:31","url":"https://arxiv.org/(...TRUNCATED) | "\\section{Introduction}\nQuantum entanglement is a key resource in quantum information with wide ap(...TRUNCATED) |
{"arxiv_id":"2302.08608","language":"en","timestamp":"2023-02-20T02:03:03","url":"https://arxiv.org/(...TRUNCATED) | "\\section{Introduction}\nIn this paper, we build on an existing body of work that examines the extr(...TRUNCATED) |
{"arxiv_id":"2302.08607","language":"en","timestamp":"2023-02-20T02:03:03","url":"https://arxiv.org/(...TRUNCATED) | "\\section{Introduction}\nAccurate and efficient speech recognition models are key to realizing auto(...TRUNCATED) |
End of preview. Expand in Data Studio
ArXiv papers from RedPajama-Data originally published in February 2023
We collect the ArXiv papers released shortly before the training data cutoff date for the OpenLLaMA models.
The OpenLLaMA models (V1) have been trained on RedPajama data. The last batch of ArXiv papers included in this dataset are papers published in February 2023. To get the members close to the cutoff data, we collect the 13,155 papers published in "2302" as part of the training dataset. We process the raw LateX files using this script.
This dataset has been used as source for 'member' documents to develop (document-level) MIAs against LLMs using data collected shortly before (member) and after (non-member) the training cutoff date for the target model (the suite of OpenLLaMA models). For non-members for the RDD setup, we refer to our Github repo. For more details and results see the section of Regression Discontiuity Design (RDD) in the paper "SoK: Membership Inference Attacks on LLMs are Rushing Nowhere (and How to Fix It)".
- Downloads last month
- 3