Results averaged over ResNet18 and ResNet50.

For this, we utilize an auxiliary contrastive model with the popular heuristic that biased features are learned preferentially during training.

Revealed: The message Norwegian PM sent Trump that sparked president's outburst saying Nobel Peace Prize snub justified Greenland land-grab The Kristi Noem photo that reveals why anti-ICE mob stormed Minnesota church as terrified child worshipper sobbed in father's arms We were $460,000 in debt. We weren't high earners and paid it off using simple but life-changing tricks... anyone can do it Idyllic city was hit by a surge in cancers and miscarriages when Trump's'beautiful baby' arrived. Joseph Gordon-Levitt was the hottest actor in Hollywood... then vanished: Unearthing family tragedy that sparked disappearance and has left'lasting' scars Mayor of gorgeous Oregon city that's home to Nike HQ explains simple reasons why it is thriving while neighboring Portland circles the drain Spanish train disaster victims flew through windows and were found hundreds of yards away, with more than 39 feared dead - as mystery over what caused'truly strange' crash grows Dark side of America's favorite vacation hotspot... where women are subjected to the most horrific sex attacks imaginable Dietitian reveals the game-changing supplements that work like Ozempic... and will super-charge your weight loss without side-effects Pierce Brosnan fans defend star's wife Keely Shaye Smith, 62, after cruel troll posts a photo of her when she met the 72-year-old Bond star alongside a recent snap as a'reminder to avoid marriage' My husband was acting odd for months. But nothing prepared me for what was hidden under the couch... undeniable proof of my worst fear John Barrowman breaks down in tears while cradling his dog's body after the beloved pet'waited until I got home' before dying peacefully in his arms Country singer Karley Scott Collins responds to rumors she's living with Keith Urban after Nicole Kidman split Is AI making us STUPID? Latest episode of Daily Mail's Wellness Explained examines what our increasing reliance on ChatGPT is doing to our brains Artificial Intelligence ( AI) chatbots like ChatGPT are now a daily part of life for millions of people - but what is that really doing to our brains?

As Europe's longstanding alliance with the US falters, its push to become a self-sufficient AI superpower has become more urgent. As the relationship between the US and its European allies shows signs of strain, AI labs across the continent are searching for inventive ways to close the gap with American rivals that have so far dominated the field. With rare exceptions, US-based firms outstrip European competitors across the AI production line--from processor design and manufacturing, to datacenter capacity, to model and application development. Likewise, the US has captured a massive proportion of the money pouring into AI, reflected in the performance last year of its homegrown stocks and the growth of its econonmy . The belief in some quarters is that the US-based leaders --Nvidia, Google, Meta, OpenAI, Anthropic, and the like--are already so entrenched as to make it impossible for European nations to break their dependency on American AI, mirroring the pattern in cloud services.

In many classification datasets, the task labels are spuriously correlated with some input attributes. Classifiers trained on such datasets often rely on these attributes for prediction, especially when the spurious correlation is high, and thus fail togeneralize whenever there is a shift in the attributes' correlation at deployment. If we assume that the spurious attributes are known a priori, several methods have been proposed to learn a classifier that is invariant to the specified attributes. However, in real-world data, information about spurious attributes is typically unavailable. Therefore, we propose a method that automatically identifies spurious attributes by estimating their causal effect on the label and then uses a regularization objective to mitigate the classifier's reliance on them.

Transformer models have become increasingly popular in financial applications, yet their potential risk making and biases remain under-explored. The purpose of this work is to audit the reliance of the model on volatile data for decision-making, and quantify how the frequency of price movements affects the model's prediction confidence. We employ a transformer model for prediction, and introduce a metric based on Partial Information Decomposition (PID) to measure the influence of each asset on the model's decision making. Our analysis reveals two key observations: first, the model disregards data volatility entirely, and second, it is biased toward data with lower-frequency price movements.

Explanations are often promoted as tools for transparency, but they can also foster confirmation bias; users may assume reasoning is correct whenever outputs appear acceptable. We study this double-edged role of Chain-of-Thought (CoT) explanations in multimodal moral scenarios by systematically perturbing reasoning chains and manipulating delivery tones. Specifically, we analyze reasoning errors in vision language models (VLMs) and how they impact user trust and the ability to detect errors. Our findings reveal two key effects: (1) users often equate trust with outcome agreement, sustaining reliance even when reasoning is flawed, and (2) the confident tone suppresses error detection while maintaining reliance, showing that delivery styles can override correctness. These results highlight how CoT explanations can simultaneously clarify and mislead, underscoring the need for NLP systems to provide explanations that encourage scrutiny and critical thinking rather than blind trust. All code will be released publicly.

REtrieval-Augmented LLM-based Machine Translation (REAL-MT) shows promise for knowledge-intensive tasks like idiomatic translation, but its reliability under noisy retrieval, a common challenge in real-world deployment, remains poorly understood. To address this gap, we propose a noise synthesis framework and new metrics to systematically evaluate REAL-MT's reliability across high-, medium-, and low-resource language pairs. Using both open-and closed-sourced models, including standard LLMs and large reasoning models (LRMs), we find that models heavily rely on retrieved context, and this dependence is significantly more detrimental in low-resource language pairs, producing nonsensical translations. Although LRMs possess enhanced reasoning capabilities, they show no improvement in error correction and are even more susceptible to noise, tending to rationalize incorrect contexts. Attention analysis reveals a shift from the source idiom to noisy content, while confidence increases despite declining accuracy, indicating poor self-monitoring. To mitigate these issues, we investigate training-free and fine-tuning strategies, which improve robustness at the cost of performance in clean contexts, revealing a fundamental trade-off. Our findings highlight the limitations of current approaches, underscoring the need for self-verifying integration mechanisms.