侧重于查询的摘要（QFS）需要生成使用一组相关文档的查询给出文本摘要。但是，在实践中，此类相关文件不易获得，但应首先从文档收集中检索。因此，我们展示了如何扩展此任务以使其更加逼真。因此，任务设置也类似于开放式域问题应答任务的设置，其中答案是顶部检索到的文档的摘要。要解决此扩展任务，我们将通过文本生成组合通过文本生成来产生给定输入查询的检索段落的摘要。我们展示了第一个对拟议任务的评估结果，并表明一些样本足以通过检索的通道进行微调的大型生成模型。

Large language models have recently attracted significant attention due to their impressive performance on a variety of tasks. ChatGPT developed by OpenAI is one such implementation of a large, pre-trained language model that has gained immense popularity among early adopters, where certain users go to the extent of characterizing it as a disruptive technology in many domains. Understanding such early adopters' sentiments is important because it can provide insights into the potential success or failure of the technology, as well as its strengths and weaknesses. In this paper, we conduct a mixed-method study using 10,732 tweets from early ChatGPT users. We first use topic modelling to identify the main topics and then perform an in-depth qualitative sentiment analysis of each topic. Our results show that the majority of the early adopters have expressed overwhelmingly positive sentiments related to topics such as Disruptions to software development, Entertainment and exercising creativity. Only a limited percentage of users expressed concerns about issues such as the potential for misuse of ChatGPT, especially regarding topics such as Impact on educational aspects. We discuss these findings by providing specific examples for each topic and then detail implications related to addressing these concerns for both researchers and users.

Deep learning-based linkage of records across different databases is becoming increasingly useful in data integration and mining applications to discover new insights from multiple sources of data. However, due to privacy and confidentiality concerns, organisations often are not willing or allowed to share their sensitive data with any external parties, thus making it challenging to build/train deep learning models for record linkage across different organizations' databases. To overcome this limitation, we propose the first deep learning-based multi-party privacy-preserving record linkage (PPRL) protocol that can be used to link sensitive databases held by multiple different organisations. In our approach, each database owner first trains a local deep learning model, which is then uploaded to a secure environment and securely aggregated to create a global model. The global model is then used by a linkage unit to distinguish unlabelled record pairs as matches and non-matches. We utilise differential privacy to achieve provable privacy protection against re-identification attacks. We evaluate the linkage quality and scalability of our approach using several large real-world databases, showing that it can achieve high linkage quality while providing sufficient privacy protection against existing attacks.

基于K-Nearest的邻居（KNN）的深度学习方法，由于其简单性和几何解释性，已应用于许多应用。但是，尚未对基于KNN的分类模型的鲁棒性进行彻底探索，而KNN攻击策略欠发达。在本文中，我们提出了对敌对的软knn（询问）损失，以设计更有效的KNN攻击策略，并为他们提供更好的防御能力。我们的问损失方法有两个优势。首先，与以前的作品中提出的目标相比，问问损失可以更好地近似KNN分类错误的可能性。其次，询问损失是可以解释的：它保留了扰动输入和课堂参考数据之间的相互信息。我们使用询问损失来生成一种名为Ask-Attack（Ask-ATK）的新颖攻击方法，该方法显示出相对于先前的KNN攻击，显示出了卓越的攻击效率和准确性降解。然后，基于Ask-ATK，我们得出了一个Ask \ supessline {def} ense（ask-def）方法，该方法优化了Ask-ATK引起的最坏情况训练损失。 CIFAR-10（IMAGENET）上的实验表明，（i）Ask-Atk成就$ \ geq 13 \％$（$ \ geq 13 \％$）提高了先前的KNN攻击的攻击成功率，以及（ii）ask-def $ \ geq 6.9 \％$（$ \ geq 3.5 \％$）在稳健性改善方面胜过常规的对抗训练方法。