Multilingual question answering (QA) systems must ensure factual consistency across languages, especially for objective queries such as What is jaundice?, while also accounting for cultural variation in subjective responses. We propose MIND, a user-in-the-loop fact-checking pipeline to detect factual and cultural discrepancies in multilingual QA knowledge bases. MIND highlights divergent answers to culturally sensitive questions (e.g., Who assists in childbirth?) that vary by region and context. We evaluate MIND on a bilingual QA system in the maternal and infant health domain and release a dataset of bilingual questions annotated for factual and cultural inconsistencies. We further test MIND on datasets from other domains to assess generalization. In all cases, MIND reliably identifies inconsistencies, supporting the development of more culturally aware and factually consistent QA systems.

Things to Do in L.A. Tap to enable a layout that focuses on the article. CDC warns of dramatic rise in dangerous drug-resistant bacteria. The Centers for Disease Control and Prevention warned in a report this week that infections caused by a "super bug" bacteria surged by more than 460% in the United States between 2019 and 2023. This is read by an automated voice. Please report any issues or inconsistencies here .

The generation of incorrect images, such as depictions of people of color in Nazi-era uniforms by Gemini, frustrated users and harmed Google's reputation, motivating us to investigate the relationship between accurately reflecting factuality and promoting diversity and equity. In this study, we focus on 19 real-world statistics collected from authoritative sources. Using these statistics, we develop a checklist comprising objective and subjective queries to analyze behavior of large language models (LLMs) and text-to-image (T2I) models. Objective queries assess the models' ability to provide accurate world knowledge. In contrast, the design of subjective queries follows a key principle: statistical or experiential priors should not be overgeneralized to individuals, ensuring that models uphold diversity. These subjective queries are based on three common human cognitive errors that often result in social biases. We propose metrics to assess factuality and fairness, and formally prove the inherent trade-off between these two aspects. Results show that GPT-4o and DALL-E 3 perform notably well among six LLMs and four T2I models. Our code is publicly available at https://github.com/uclanlp/Fact-or-Fair.

DPSGD to fine-tune these models on private data often yields poor results, particularly when the private Differentially private SGD (DPSGD) enables dataset is small (Tramèr et al., 2022; Mireshghallah privacy-preserving training of language models, et al., 2021). Recent work has shown that leveraging but often reduces utility, diversity, and linguistic better hand-crafted features (Tramer and Boneh, 2020) quality. We introduce DPRefine, a threephase or features from large pre-trained language models (Li method that initializes a model using et al., 2022, 2021) can improve the privacy-utility tradeoff data synthesis from a small pre-trained LM in differentially private learning. However, these with rigorous filtering, applies DP finetuning approaches have limitations: smaller pre-trained models on private data, and performs self-distillation offer limited benefits, and fine-tuning larger models on to refine outputs. This approach significantly private data may be infeasible due to proprietary concerns outperforms vanilla DPSGD, with AlpacaEval or infrastructure limitations. This raises a critical preferring DPRefine's generations in 78.4% question: Can we develop small, domain-specific language of cases across all datasets. Our analysis reveals models that achieve high performance without that DPRefine reduces linguistic errors in requiring large private datasets or large, pre-trained generated text by 84.0%, mitigating grammar models?

Synthetic opioids are the most common drugs involved in drug-involved overdose mortalities in the U.S. The Center for Disease Control and Prevention reported that in 2018, about 70% of all drug overdose deaths involved opioids and 67% of all opioid-involved deaths were accounted for by synthetic opioids. In this study, we investigated the spread of synthetic opioids between 2013 and 2020 in the U.S. We analyzed the relationship between the spatiotemporal pattern of synthetic opioid-involved deaths and another key opioid, heroin, and compared patterns of deaths involving these two types of drugs during this period. Spatial connections and human mobility between counties were incorporated into a graph convolutional neural network model to represent and analyze the spread of synthetic opioid-involved deaths in the context of previous heroin-involved death patterns.

Over the last ten years, the US Centers for Disease Control and Prevention (CDC) has organized an annual influenza forecasting challenge with the motivation that accurate probabilistic forecasts could improve situational awareness and yield more effective public health actions. Starting with the 2021/22 influenza season, the forecasting targets for this challenge have been based on hospital admissions reported in the CDC's National Healthcare Safety Network (NHSN) surveillance system. Reporting of influenza hospital admissions through NHSN began within the last few years, and as such only a limited amount of historical data are available for this signal. To produce forecasts in the presence of limited data for the target surveillance system, we augmented these data with two signals that have a longer historical record: 1) ILI+, which estimates the proportion of outpatient doctor visits where the patient has influenza; and 2) rates of laboratory-confirmed influenza hospitalizations at a selected set of healthcare facilities. Our model, Flusion, is an ensemble that combines gradient boosting quantile regression models with a Bayesian autoregressive model. The gradient boosting models were trained on all three data signals, while the autoregressive model was trained on only the target signal; all models were trained jointly on data for multiple locations. Flusion was the top-performing model in the CDC's influenza prediction challenge for the 2023/24 season. In this article we investigate the factors contributing to Flusion's success, and we find that its strong performance was primarily driven by the use of a gradient boosting model that was trained jointly on data from multiple surveillance signals and locations. These results indicate the value of sharing information across locations and surveillance signals, especially when doing so adds to the pool of available training data.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Northeastern University in Boston will be given $17.5 million by the Centers for Disease Control and Prevention (CDC) to lead an innovation center focused on infectious disease detection and preparation, the university announced. The Center for Advanced Epidemic Analytics and Predictive Modeling Technology, or EPISTORM, will "help detect and prepare the United States for the next outbreak of infectious disease, especially in rural areas," according to the university's Northeastern Global News (NGN). The funds will be used to coordinate the work of various consortium members across the U.S. to prepare local communities for outbreaks, including RSV and the seasonal flu.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' Researchers are proposing using artificial intelligence technology to help diagnose autism spectrum disorder. In a recent article published in Scientific Reports, researchers from Brazil, France and Germany reportedly used magnetic resonance imaging to train a machine learning algorithm. The work – in which the "quantitative diagnostic method" is proposed – was based on brain imaging data for 500 people, with more than 240 that had been diagnosed with autism. WHAT IS ARTIFICIAL INTELLIGENCE (AI)?

Doctors believe Artificial Intelligence is now saving lives, after a major advancement in breast cancer screenings. A.I. is detecting early signs of the disease, in some cases years before doctors would find the cancer on a traditional scan. Department of Veterans Affairs researchers at five medical centers are working together to develop an artificial intelligence algorithm that can predict aggressive prostate cancer. The new research study began July 1, expanding to 14 sites. It will analyze data from more than 5,000 veterans who were diagnosed with high-risk prostate cancer and have undergone initial treatment.

Chief Washington correspondent Mike Emanuel reports the latest on the new Alzheimer's drug. A new artificial intelligence-powered tool aims to detect signs of dementia, Alzheimer's and other memory disorders by analyzing a person's speech and language patterns. The system is called CognoSpeak. Researchers at the University of Sheffield in the U.K. developed it. In early trials that included both Alzheimer's patients and cognitively heathy people, the tool showed 90% accuracy in identifying those with dementia -- which is just as accurate as "pen-and-paper tests," according to a press release announcing the new tool.