Fury as NYC on course to join Detroit, Chicago and Puerto Rico with woke mayor Mamdani's latest reckless plan Hidden $65bn lithium motherlode mapped beneath America's oldest mountains could power nation for centuries A quarter of US stock market gets report cards from Wall Street on same day this week. Even one bad grade can spell catastrophe for your 401(k). Here's EXACTLY what you need to do I was constantly burned out and kept cancelling plans because I was so tired. Doctors said it was just hormones... then I was diagnosed with this aggressive cancer. Nicole Kidman's daughters have'CUT OFF' dad Keith Urban: Insiders reveal why they are'SO angry'... and how he is utterly'distraught' but finally admitting'guilt' Florida go-kart park ordered to pay hefty settlement after mom and daughter, 6, broke two important rules that resulted in little girl's death King Charles leaves White House roaring with laughter with jokes to Trump about'speaking French' and the Boston Tea Party in dazzling state dinner Brace for the'Big Crunch': Scientists predict when the universe will end - and it's TRILLIONS of years sooner than we thought The $1.50 fruit that can protect you from deadly heart disease Why Donald Trump Jr and Bettina Anderson's wedding is'on hold' just weeks after extravagant'enchanted garden' bridal shower Serena Williams leaves fans split with controversial parenting confession as tennis legend opens up on'discipline' incident with daughter'No more Mr Nice Guy!': Trump warns Iran to'get smart' and'sign non-nuclear deal' with image of him brandishing assault rifle - as oil prices spike once more The surprise state cashing in big as Californians flee in droves... and the $672-a-month reason why What REALLY goes on in some Equinox steam rooms: Gym insiders reveal eye-popping indecency... secret towel signals used by experimental married men... and clubs with most'aggressive' locker rooms Fox News's Jesse Watters, 47, takes his young wife, 33, to state dinner after causing stir with story of how he seduced her Truth about Jordan Peterson's catastrophic decline: Inside his living hell, dumbstruck and in'overwhelming pain' locked up on $50m estate... as friends point finger about REAL cause Worrying shift as restaurant chain rolls out no-seating stores - sparking fears this is just the start of a'corporate purge of the American dining room' Shocking footage has revealed the moment a dancing robot went rogue at a school sports day.

We find that the distribution shift settings in previous studies commonly lack adequate challenges, hindering the accurate evaluation of OOD robustness. To address these issues, we propose a benchmark construction protocol that ensures clear differentiation and challenging distribution shifts. Then we introduce BOSS, a Benchmark suite for Out-of-distribution robustneSS evaluation covering 5 tasks and 20 datasets. Based on BOSS, we conduct a series of experiments on pretrained language models for analysis and evaluation of OOD robustness. First, for vanilla fine-tuning, we examine the relationship between in-distribution (ID) and OOD performance. We identify three typical types that unveil the inner learning mechanism, which could potentially facilitate the forecasting of OOD robustness, correlating with the advancements on ID datasets. Then, we evaluate 5 classic methods on BOSS and find that, despite exhibiting some effectiveness in specific cases, they do not offer significant improvement compared to vanilla fine-tuning. Further, we evaluate 5 LLMs with various adaptation paradigms and find that when sufficient ID data is available, fine-tuning domain-specific models outperform LLMs on ID examples significantly.

Self-supervised learning (SSL) is a powerful tool in machine learning, but understanding the learned representations and their underlying mechanisms remains a challenge. This paper presents an in-depth empirical analysis of SSL-trained representations, encompassing diverse models, architectures, and hyperparameters. Our study reveals an intriguing aspect of the SSL training process: it inherently facilitates the clustering of samples with respect to semantic labels, which is surprisingly driven by the SSL objective's regularization term. This clustering process not only enhances downstream classification but also compresses the data information. Furthermore, we establish that SSL-trained representations align more closely with semantic classes rather than random classes. Remarkably, we show that learned representations align with semantic classes across various hierarchical levels, and this alignment increases during training and when moving deeper into the network. Our findings provide valuable insights into SSL's representation learning mechanisms and their impact on performance across different sets of classes.

HCCV datasets constructed through nonconsensual web scraping lack crucial metadata for comprehensive fairness and robustness evaluations. Current remedies are post hoc, lack persuasive justification for adoption, or fail to provide proper contextualization for appropriate application. Our research focuses on proactive, domain-specific recommendations, covering purpose, privacy and consent, and diversity, for curating HCCV evaluation datasets, addressing privacy and bias concerns. We adopt an ante hoc reflective perspective, drawing from current practices, guidelines, dataset withdrawals, and audits, to inform our considerations and recommendations.