Large language models (LLMs) inevitably memorize sensitive, copyrighted, and
harmful knowledge from the training corpus; therefore, it is crucial to erase
this knowledge from the models. Machine unlearning is a promising solution for
efficiently removing specific knowledge by post hoc modifying models. In this
paper, we propose a Real-World Knowledge Unlearning benchmark (RWKU) for LLM
unlearning. RWKU is designed based on the following three key factors: (1) For
the task setting, we consider a more practical and challenging unlearning
setting, where neither the forget corpus nor the retain corpus is accessible.
(2) For the knowledge source, we choose 200 real-world famous people as the
unlearning targets and show that such popular knowledge is widely present in
various LLMs. (3) For the evaluation framework, we design the forget set and
the retain set to evaluate the model's capabilities across various real-world
applications. Regarding the forget set, we provide four four membership
inference attack (MIA) methods and nine kinds of adversarial attack probes to
rigorously test unlearning efficacy. Regarding the retain set, we assess
locality and utility in terms of neighbor perturbation, general ability,
reasoning ability, truthfulness, factuality, and fluency. We conduct extensive
experiments across two unlearning scenarios, two models and six baseline
methods and obtain some meaningful findings. We release our benchmark and code
publicly at this http URL for future work.

机器遗忘是一种有效地通过事后修改模型来消除特定知识的方法，在本文中，我们提出了一个用于大型语言模型（LLMs）遗忘的真实世界知识遗忘基准（RWKU），该基准考虑了任务设置、知识来源和评估框架等关键因素。

RWKU：大规模语言模型的真实世界知识遗忘基准测试

RWKU: Benchmarking Real-World Knowledge Unlearning for Large Language  Models

In recent years, large language models (LLMs) have spurred a new research
paradigm in natural language processing. Despite their excellent capability in
knowledge-based question answering and reasoning, their potential to retain
faulty or even harmful knowledge poses risks of malicious application. The
challenge of mitigating this issue and transforming these models into purer
assistants is crucial for their widespread applicability. Unfortunately,
Retraining LLMs repeatedly to eliminate undesirable knowledge is impractical
due to their immense parameters. Knowledge unlearning, derived from analogous
studies on machine unlearning, presents a promising avenue to address this
concern and is notably advantageous in the context of LLMs. It allows for the
removal of harmful knowledge in an efficient manner, without affecting
unrelated knowledge in the model. To this end, we provide a survey of knowledge
unlearning in the era of LLMs. Firstly, we formally define the knowledge
unlearning problem and distinguish it from related works. Subsequently, we
categorize existing knowledge unlearning methods into three classes: those
based on parameter optimization, parameter merging, and in-context learning,
and introduce details of these unlearning methods. We further present
evaluation datasets used in existing methods, and finally conclude this survey
by presenting the ongoing challenges and future directions.

近年来，大型语言模型（LLM）在自然语言处理领域引起了一种新的研究范式。然而，这些模型保留错误或甚至有害知识的潜力带来了恶意应用的风险。为了使这些模型能够广泛应用，解决这个问题并将其转化为纯粹的助手是至关重要的。为此，我们提供了一份关于 LLM 时代的知识遗忘的综述。我们从正式定义知识遗忘问题并与相关工作区分开始。随后，我们将现有的知识遗忘方法分为三类：基于参数优化、参数合并和上下文学习的方法，并介绍这些方法的细节。我们还介绍了现有方法中使用的评估数据集，并最后总结了这份综述中存在的挑战和未来的方向。

LLM 知识弃除：任务、方法与挑战

Knowledge Unlearning for LLMs: Tasks, Methods, and Challenges

Pretrained Language Models (LMs) memorize a vast amount of knowledge during
initial pretraining, including information that may violate the privacy of
personal lives and identities. Previous work addressing privacy issues for
language models has mostly focused on data preprocessing and differential
privacy methods, both requiring re-training the underlying LM. We propose
knowledge unlearning as an alternative method to reduce privacy risks for LMs
post hoc. We show that simply performing gradient ascent on target token
sequences is effective at forgetting them with little to no degradation of
general language modeling performances for larger LMs; it sometimes even
substantially improves the underlying LM with just a few iterations. We also
find that sequential unlearning is better than trying to unlearn all the data
at once and that unlearning is highly dependent on which kind of data (domain)
is forgotten. By showing comparisons with a previous data preprocessing method
and a decoding method known to mitigate privacy risks for LMs, we show that
unlearning can give a stronger empirical privacy guarantee in scenarios where
the data vulnerable to extraction attacks are known a priori while being much
more efficient and robust. We release the code and dataset needed to replicate
our results at this https URL.

本研究提出知识遗忘（knowledge unlearning）作为一种减少预训练语言模型（pretrained language models）隐私风险的方法，通过对目标标记序列进行梯度上升来遗忘它们，并发现顺序遗忘优于尝试一次性遗忘所有数据，对于特定领域的数据，知识遗忘具有更强的实证隐私保证，同时更加高效和鲁棒。