Training large language models (LLMs) is a costly endeavour in terms of time
and computational resources. The large amount of training data used during the
unsupervised pre-training phase makes it difficult to verify all data and,
unfortunately, undesirable data may be ingested during training. Re-training
from scratch is impractical and has led to the creation of the 'unlearning'
discipline where models are modified to "unlearn" undesirable information
without retraining. However, any modification can alter the behaviour of LLMs,
especially on key dimensions such as fairness. This is the first work that
examines this interplay between unlearning and fairness for LLMs. In
particular, we focus on a popular unlearning framework known as SISA [Bourtoule
et al., 2021], which creates an ensemble of models trained on disjoint shards.
We evaluate the performance-fairness trade-off for SISA, and empirically
demsontrate that SISA can indeed reduce fairness in LLMs. To remedy this, we
propose post-processing bias mitigation techniques for ensemble models produced
by SISA. We adapt the post-processing fairness improvement technique from
[Hardt et al., 2016] to design three methods that can handle model ensembles,
and prove that one of the methods is an optimal fair predictor for ensemble of
models. Through experimental results, we demonstrate the efficacy of our
post-processing framework called 'FairSISA'.

这篇论文研究了大型语言模型（LLMs）的反学习和公平性之间的相互作用，特别关注了一种称为 SISA 的流行反学习框架的性能 - 公平性权衡，并提出了后处理偏差缓解技术用于 SISA 生成的模型集合，证明其中的一种方法是模型集合的最佳公平预测器，在实验证明了我们提出的后处理框架 'FairSISA' 的有效性。

公平 SISA：集成后处理以提高 LLMs 的公平性

FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in  LLMs

Machine unlearning is a process of removing the impact of some training data
from the machine learning (ML) models upon receiving removal requests. While
straightforward and legitimate, retraining the ML model from scratch incurs a
high computational overhead. To address this issue, a number of approximate
algorithms have been proposed in the domain of image and text data, among which
SISA is the state-of-the-art solution. It randomly partitions the training set
into multiple shards and trains a constituent model for each shard. However,
directly applying SISA to the graph data can severely damage the graph
structural information, and thereby the resulting ML model utility. In this
paper, we propose GraphEraser, a novel machine unlearning framework tailored to
graph data. Its contributions include two novel graph partition algorithms and
a learning-based aggregation method. We conduct extensive experiments on five
real-world graph datasets to illustrate the unlearning efficiency and model
utility of GraphEraser. It achieves 2.06$\times$ (small dataset) to
35.94$\times$ (large dataset) unlearning time improvement. On the other hand,
GraphEraser achieves up to $62.5\%$ higher F1 score and our proposed
learning-based aggregation method achieves up to $112\%$ higher F1
score.\footnote{Our code is available at
https://github.com/MinChen00/Graph-Unlearning.}

本文提出了一种专门针对图数据的新型机器取消学习框架 GraphEraser，包括两种新颖的图分区算法和一种基于学习的聚合方法。研究表明，与 SISA 相比，GraphEraser 在取消学习效率和模型效用方面都具有较大优势。