Often, multilingual language models are trained with the objective to map semantically similar content (in different languages) in the same latent space. In this paper, we show a nuance in this training objective, and find that by changing the language of the input query, we can improve the question answering ability of language models. Our contributions are two-fold. First, we introduce the term Language Specific Knowledge (LSK) to denote queries that are best answered in an "expert language" for a given LLM, thereby enhancing its question-answering ability. We introduce the problem of language selection -- for some queries, language models can perform better when queried in languages other than English, sometimes even better in low-resource languages -- and the goal is to select the optimal language for the query. Second, we introduce simple to strong baselines to test this problem. Additionally, as a first-pass solution to this novel problem, we design LSKExtractor to benchmark the language-specific knowledge present in a language model and then exploit it during inference. To test our framework, we employ three datasets that contain knowledge about both cultural and social behavioral norms. Overall, LSKExtractor achieves up to 10% relative improvement across datasets, and is competitive against strong baselines, while being feasible in real-world settings. Broadly, our research contributes to the open-source development (https://github.com/agarwalishika/LSKExtractor/tree/main) of language models that are inclusive and more aligned with the cultural and linguistic contexts in which they are deployed.

Bureau of Meteorology, Australia ABSTRACT: Artificial Intelligence (AI) weather models are now reaching operational-grade performance for some variables, but like traditional Numerical Weather Prediction (NWP) models, they exhibit systematic biases and reliability issues. We test the application of the Bureau of Meteorology's existing statistical post-processing system, IMPROVER, to ECMWF's deterministic Artificial Intelligence Forecasting System (AIFS), and compare results against post-processed outputs from the ECMWF HRES and ENS models. Without any modification to processing workflows, post-processing yields comparable accuracy improvements for AIFS as for traditional NWP forecasts, in both expected value and probabilistic outputs. We show that blending AIFS with NWP models improves overall forecast skill, even when AIFS alone is not the most accurate component. These findings show that statistical post-processing methods developed for NWP are directly applicable to AI models, enabling national meteorological centres to incorporate AI forecasts into existing workflows in a low-risk, incremental fashion. Notice This Work has been accepted by Artificial Intelligence for the Earth Systems. The AMS does not guarantee that the copy provided here is an accurate copy of the Version of Record (VoR).

Supervised learning relies on high-quality labeled data, but obtaining such data through human annotation is both expensive and time-consuming. Recent work explores using large language models (LLMs) for annotation, but LLM-generated labels still fall short of human-level quality. To address this problem, we propose the Annotation with Critical Thinking (ACT) data pipeline, where LLMs serve not only as annotators but also as judges to critically identify potential errors. Human effort is then directed towards reviewing only the most "suspicious" cases, significantly improving the human annotation efficiency. Our major contributions are as follows: (1) ACT is applicable to a wide range of domains, including natural language processing (NLP), computer vision (CV), and multimodal understanding, by leveraging multimodal-LLMs (MLLMs). (2) Through empirical studies, we derive 7 insights on how to enhance annotation quality while efficiently reducing the human cost, and then translate these findings into user-friendly guidelines. (3) We theoretically analyze how to modify the loss function so that models trained on ACT data achieve similar performance to those trained on fully human-annotated data. Our experiments show that the performance gap can be reduced to less than 2% on most benchmark datasets while saving up to 90% of human costs.

Abstract--This paper aims to address the challenge of selecting relevant courses for students by proposing the design and development of a course recommendation system. The course recommendation system utilises a combination of data analytics techniques and machine learning algorithms to recommend courses that align with current industry trends and requirements. In order to provide customised suggestions, the study entails the design and implementation of an extensive algorithmic framework that combines machine learning methods, user preferences, and academic criteria. The system employs data mining and collaborative filtering techniques to examine past courses and individual career goals in order to provide course recommendations. Moreover, to improve the accessibility and usefulness of the recommendation system, special attention is given to the development of an easy-to-use front-end interface. We refined and optimised the proposed system by incorporating user feedback, ensuring that it effectively meets the needs and preferences of its target users. The proposed course recommendation system could be a useful tool for students, instructors, and career advisers to use in promoting lifelong learning and professional progression as it fills the gap between university learning and industry expectations. We hope that the proposed course recommendation system will help university students in making data-drive and industry-informed course decisions, in turn, improving graduate outcomes for the university sector .

The rapid advancement of generative models has enabled the creation of increasingly stealthy synthetic voices, commonly referred to as audio deepfakes. A recent technique, FOICE [USENIX'24], demonstrates a particularly alarming capability: generating a victim's voice from a single facial image, without requiring any voice sample. By exploiting correlations between facial and vocal features, FOICE produces synthetic voices realistic enough to bypass industry-standard authentication systems, including WeChat Voiceprint and Microsoft Azure. This raises serious security concerns, as facial images are far easier for adversaries to obtain than voice samples, dramatically lowering the barrier to large-scale attacks. In this work, we investigate two core research questions: (RQ1) can state-of-the-art audio deepfake detectors reliably detect FOICE-generated speech under clean and noisy conditions, and (RQ2) whether fine-tuning these detectors on FOICE data improves detection without overfitting, thereby preserving robustness to unseen voice generators such as SpeechT5. Our study makes three contributions. First, we present the first systematic evaluation of FOICE detection, showing that leading detectors consistently fail under both standard and noisy conditions. Second, we introduce targeted fine-tuning strategies that capture FOICE-specific artifacts, yielding significant accuracy improvements. Third, we assess generalization after fine-tuning, revealing trade-offs between specialization to FOICE and robustness to unseen synthesis pipelines. These findings expose fundamental weaknesses in today's defenses and motivate new architectures and training protocols for next-generation audio deepfake detection.

Billionaire UK businessman Joe Lewis, whose family trust owns Tottenham Hotspur football club, has received a pardon from US President Donald Trump. Lewis, 88, pleaded guilty to insider trading as part of an agreement with prosecutors in 2024 that saw him avoid prison. He was accused of passing on information about his companies to his private pilots, friends, personal assistants and romantic partners in a fraud that authorities said netted millions of dollars in profit. A White House official said Trump approved the pardon for Lewis, who requested it so he could receive medical treatment and visit his grandchildren and great grandchildren in the US. Mr Lewis admitted he made a terrible mistake, did not fight extradition in the case, and paid a $5 million fine, the official told the BBC.

By 2033, the global AI chip market is projected to reach $700bn (£620bn) a year, outstripping the whole of today's semiconductor market. By 2033, the global AI chip market is projected to reach $700bn (£620bn) a year, outstripping the whole of today's semiconductor market. Britain's legacy in chip design is world-class, and we could supply up to 5% of global demand if we get our act together Thu 13 Nov 2025 13.26 ESTLast modified on Thu 13 Nov 2025 14.08 EST The UK is in a uniquely promising position, far too little understood, to play a lucrative role in the coming era of artificial intelligence - but only if it also grabs the opportunity to start making millions of computer chips. AI requires vast numbers of chips and we could supply up to 5% of world demand if we get our national act together. Our legacy in chip design is world-class, starting with the first general-purpose electronic computer, the first electronic memory and the first parallel computer.

An independent review of the Sara Sharif case has identified multiple failings from agencies before her murder in Surrey in 2023, following two years of abuse. The child safeguarding practice review, published on Thursday, said there were clearly several points in Sara's life, in particular during the last few months, where different actions could and should have been taken by the authorities. The system failed to keep her safe, it added. Responding to the report, the Children's Commissioner said the case was a catalogue of missed opportunities, poor communication and ill-informed assumptions. The education secretary said there had been the glaring failures across all agencies.

Harry and Meghan's photo-gate leaves Kardashian clan'upset': Sussexes demanded not to be pictured inside Kris Jenner's 70th birthday party before mystery deletion Epstein's ultimate betrayal of Trump as emails reveal billionaire's twisted plot against president: 'I am the one able to take him down' Father of cheerleader who mysteriously died on Carnival cruise speaks out on investigation... and reveals the horrific theories he's heard I tried the'magic' pill that claims to cure migraines, back pain, anxiety and insomnia. The relief was instant... and it costs just $25 a month Kim Kardashian's daughter North West, 12, shocks fans with'high-risk piercing' not suitable for kids Alex Murdaugh's housekeeper says she KNEW the lawyer killed his wife and son in bombshell new book Civil rights leader Rev. Jesse Jackson hospitalized in Chicago Donald Trump leaves Ozzy Osbourne's widow Sharon in tears after paying tribute to the late rocker Kelly Clarkson's staff'feel like s***': TV insiders reveal star's huge backstage transformation after death of ex-husband He killed his daughter, 2, in a hot car then committed suicide on day he was due to be jailed. Then she tried to have her rich husband assassinated. Epstein's mysterious falling out with Clinton is revealed in emails to Obama lawyer inviting her to his infamous NYC townhouse John Travolta's son Benjamin, 14, has grown into his spitting image as Grease star proudly shares new clip Sober Dolphins coach Mike McDaniel'indebted' to Commanders' Dan Quinn for helping him beat drinking problem Diddy has prison release date pushed BACK amid allegations of'drinking moonshine' Kill a comrade or be killed: Three winters into Putin's war, his army is devouring itself. Trump makes sordid joke about Muslim president's WIFE at the White House The Navy commander who stared down Al Qaeda on the USS Cole has a new enemy... and a chilling warning for America Swear words that were once potent are losing their sting, a new study has revealed.