Machine Unlearning of Pre-trained Large Language Models
Paper โข 2402.15159 โข Published
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Lobo A, Hansen JK, Hansen LN, Kjรฆr ED. Differences among six woody perennials native to Northern Europe in their level of genetic differentiation and adaptive potential at fine local scale. Ecol Evol. 2018;8:2231--2239. <https://doi.org/10.1002/ece3.3824>
1. INTRODUCTION {#ece33824-sec-0001}
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Phenology... |
Introduction {#s1}
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Understanding cell migration mechanisms is a critical issue in many biophysical phenomena, including angiogenesis, tumor growth, metastasis, and wound healing [@pcbi.1002926-Condeelis1]--[@pcbi.1002926-Li1]. Cell migration is a complex multifaceted process, triggered by chemotaxis and h... |
\section{Introduction}
Reducing optical losses is of paramount importance for further developing photovoltaic (PV) devices. This holds especially true for the market-dominating single-junction c-Si solar cells, for which optical losses constitute one of the main limitations to reach their efficiency limit~\cite{shockle... |
\section{Results}
Evaluation is performed on 2 test datasets, each containing 5 keyframes. Similarly to the training data, keyframes 1-4 all have interpolation sequences from keyframe $N$ to keyframe $N+1$ and keyframe 5 is a single image. The metric we use is the mean absolute error in mm of the depth measurement at... |
1.. Introduction
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Breathing is one of the essential functions for the survival of most living beings. Many processes to measure the respiration rate have been proposed: using a stretch sensor or impedance meter to detect chest expansion \[[@b1-sensors-14-15371]--[@b3-sensors-14-15371]\], a pulse oximet... |
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End of preview. Expand in Data Studio
๐ unlearn_dataset
The unlearn_dataset serves as a benchmark for evaluating unlearning methodologies in pre-trained large language models across diverse domains, including arXiv, GitHub.
๐ Loading the datasets
To load the dataset:
from datasets import load_dataset
dataset = load_dataset("llmunlearn/unlearn_dataset", name="arxiv", split="forget")
- Available configuration names and corresponding splits:
arxiv:forget, approximate, retaingithub:forget, approximate, retaingeneral:evaluation, retain
๐ ๏ธ Codebase
For evaluating unlearning methods on our datasets, visit our GitHub repository.
โญ Citing our Work
If you find our codebase or dataset useful, please consider citing our paper:
@article{yao2024machine,
title={Machine Unlearning of Pre-trained Large Language Models},
author={Yao, Jin and Chien, Eli and Du, Minxin and Niu, Xinyao and Wang, Tianhao and Cheng, Zezhou and Yue, Xiang},
journal={arXiv preprint arXiv:2402.15159},
year={2024}
}
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