Document understanding models have recently demonstrated remarkable
performance by leveraging extensive collections of user documents. However,
since documents often contain large amounts of personal data, their usage can
pose a threat to user privacy and weaken the bonds of trust between humans and
AI services. In response to these concerns, legislation advocating ``the right
to be forgotten" has recently been proposed, allowing users to request the
removal of private information from computer systems and neural network models.
A novel approach, known as machine unlearning, has emerged to make AI models
forget about a particular class of data. In our research, we explore machine
unlearning for document classification problems, representing, to the best of
our knowledge, the first investigation into this area. Specifically, we
consider a realistic scenario where a remote server houses a well-trained model
and possesses only a small portion of training data. This setup is designed for
efficient forgetting manipulation. This work represents a pioneering step
towards the development of machine unlearning methods aimed at addressing
privacy concerns in document analysis applications. Our code is publicly
available at
https://github.com/leitro/MachineUnlearning-DocClassification.

研究论述了文档理解模型在用户文档集合的基础上取得的显著性能，介绍了机器遗忘的新方法，探索了将其应用于文档分类问题以解决隐私问题，并提供了公开可用的代码。

文档分类的机器去学习

Machine Unlearning for Document Classification

AI services are known to have unstable behavior when subjected to changes in
data, models or users. Such behaviors, whether triggered by omission or
commission, lead to trust issues when AI works with humans. The current
approach of assessing AI services in a black box setting, where the consumer
does not have access to the AI's source code or training data, is limited. The
consumer has to rely on the AI developer's documentation and trust that the
system has been built as stated. Further, if the AI consumer reuses the service
to build other services which they sell to their customers, the consumer is at
the risk of the service providers (both data and model providers). Our
approach, in this context, is inspired by the success of nutritional labeling
in food industry to promote health and seeks to assess and rate AI services for
trust from the perspective of an independent stakeholder. The ratings become a
means to communicate the behavior of AI systems so that the consumer is
informed about the risks and can make an informed decision. In this paper, we
will first describe recent progress in developing rating methods for text-based
machine translator AI services that have been found promising with user
studies. Then, we will outline challenges and vision for a principled,
multi-modal, causality-based rating methodologies and its implication for
decision-support in real-world scenarios like health and food recommendation.

基于食品行业中营养标签的成功，本文旨在评估和评级 AI 服务的用户信任度，并提出了面向现实世界场景如健康和食品建议的原则性、多模式、因果关系评级方法的挑战和远景。

自动化评估文本处理服务的可信度的进展

Advances in Automatically Rating the Trustworthiness of Text Processing Services

Great research interests have been attracted to devise AI services that are
able to provide mental health support. However, the lack of corpora is a main
obstacle to this research, particularly in Chinese language. In this paper, we
propose PsyQA, a Chinese dataset of psychological health support in the form of
question and answer pair. PsyQA is crawled from a Chinese mental health service
platform, and contains 22K questions and 56K long and well-structured answers.
Based on the psychological counseling theories, we annotate a portion of answer
texts with typical strategies for providing support, and further present
in-depth analysis of both lexical features and strategy patterns in the
counseling answers. We also evaluate the performance of generating counseling
answers with the generative pretrained models. Results show that utilizing
strategies enhances the fluency and helpfulness of generated answers, but there
is still a large space for future research.

本文提出 PsyQA 数据集 - 一份汇集了 2.2 万个问题和 5.6 万个答案的中文心理健康支持数据集。该数据集基于心理咨询理论进行标注，包含典型支持策略和词汇特征，同时通过使用生成预训练模型进行答案生成并取得了成功，但这方面仍有很大的研究空间。

PsyQA：中文心理健康辅导长文生成数据集

PsyQA: A Chinese Dataset for Generating Long Counseling Text for Mental  Health Support

With the global roll-out of the fifth generation (5G) networks, it is
necessary to look beyond 5G and envision the 6G networks. The 6G networks are
expected to have space-air-ground integrated networks, advanced network
virtualization, and ubiquitous intelligence. This article presents an
artificial intelligence (AI)-native network slicing architecture for 6G
networks to enable the synergy of AI and network slicing, thereby facilitating
intelligent network management and supporting emerging AI services. AI-based
solutions are first discussed across network slicing lifecycle to intelligently
manage network slices, i.e., AI for slicing. Then, network slicing solutions
are studied to support emerging AI services by constructing AI instances and
performing efficient resource management, i.e., slicing for AI. Finally, a case
study is presented, followed by a discussion of open research issues that are
essential for AI-native network slicing in 6G networks.

本文介绍了一种针对 6G 网络的人工智能本地网络切片架构，旨在促进人工智能和网络切片的协同作用，以便实现智能网络管理并支持新兴人工智能服务。