The rapid advancement and widespread use of large language models (LLMs) have raised significant concerns regarding the potential leakage of personally identifiable information (PII). These models are often trained on vast quantities of web-collected data, which may inadvertently include sensitive personal data.

Theintroduction ofDomainAdaptation (DA)techniquesaims to mitigate such performance drop when a trained agent encounters a different environment. By bridging the distribution gap between source and target domains, DA methods have shown their effect in many cross-domain tasks such as classification [27, 18], segmentation [19, 22, 23] and detection[3].

Inparticular,weconsiderthesetting where the time series data onN entities is generated from a Gaussian mixture model with autocorrelations overk clusters inRd. Our main contribution is an algorithm to construct coresets for the maximum likelihood objective for this mixture model.

Disney advert banned for showing'disturbing' severed body A menacing Disney advert featuring a severed body has been banned by the advertising regulator, which said it was likely to frighten and cause distress to children. The Advertising Standards Authority (ASA) found the entertainment giant had broken its rules with its advert for the Predator Badlands film. Parents complained that the digital poster, which featured a large alien holding aloft the severed body of a smaller, human figure, was inappropriate and disturbing for young children. Disney said the severed body was actually that of a robot, and the fact it had been cut in two further emphasised its non-human nature. The advert, which was seen on the roadside in Giffnock, Glasgow, was promoting the Disney sci-fi film ahead of its release in November.

A recent line of work aims at specially prompting LLMs to review their own generations and generate meaningful natural language feedback, which can subsequently be used to refine them [Madaan et al.,

It is commonplace to encounter heterogeneous data, of which some aspects of the data distribution may vary but the underlying causal mechanisms remain constant. When data are divided into distinct environments according to the heterogeneity, recent invariant learning methods have proposed to learn robust and invariant models using this environment partition.