The rapid advancement of Large Language Models (LLMs) has demonstrated their
vast potential across various domains, attributed to their extensive
pretraining knowledge and exceptional generalizability. However, LLMs often
encounter challenges in generating harmful content when faced with problematic
prompts. To address this problem, existing work attempted to implement a
gradient ascent based approach to prevent LLMs from producing harmful output.
While these methods can be effective, they frequently impact the model utility
in responding to normal prompts. To address this gap, we introduce Selective
Knowledge negation Unlearning (SKU), a novel unlearning framework for LLMs,
designed to eliminate harmful knowledge while preserving utility on normal
prompts. Specifically, SKU is consisted of two stages: harmful knowledge
acquisition stage and knowledge negation stage. The first stage aims to
identify and acquire harmful knowledge within the model, whereas the second is
dedicated to remove this knowledge. SKU selectively isolates and removes
harmful knowledge in model parameters, ensuring the model's performance remains
robust on normal prompts. Our experiments conducted across various LLM
architectures demonstrate that SKU identifies a good balance point between
removing harmful information and preserving utility.

通过选择性知识否定消除（SKU）框架，我们可以有效地识别和去除大语言模型中的有害知识，同时保持模型对正常提示的有效性。

通过机器遗忘实现更安全的大型语言模型

Towards Safer Large Language Models through Machine Unlearning

With the rapid development of electronic commerce, the way of shopping has
experienced a revolutionary evolution. To fully meet customers' massive and
diverse online shopping needs with quick response, the retailing AI system
needs to automatically recognize products from images and videos at the
stock-keeping unit (SKU) level with high accuracy. However, product recognition
is still a challenging task, since many of SKU-level products are fine-grained
and visually similar by a rough glimpse. Although there are already some
products benchmarks available, these datasets are either too small (limited
number of products) or noisy-labeled (lack of human labeling). In this paper,
we construct a human-labeled product image dataset named "Products-10K", which
contains 10,000 fine-grained SKU-level products frequently bought by online
customers in JD.com. Based on our new database, we also introduced several
useful tips and tricks for fine-grained product recognition. The products-10K
dataset is available via this https URL

电子商务日益发展，零售 AI 系统需要自动识别高准确度的 SKU 级别的产品，本研究构建了一个包含 10,000 个人工标注细粒度 SKU 级别产品的图像数据集，并针对细粒度产品识别提出了多种有用的技巧。