AI
Smarter, faster explanations for what you’re recommended Big AI models can explain why you’re shown a product or movie—but wiring them directly into recommendation engines often slows everything down and muddles goals. "Prism" takes a cleaner route: it splits the system into two jobs. * Oracle:
AI
Meet CLOZE Clinical notes are full of real-world details, but they’re hard for computers to use. CLOZE is a new AI framework that reads these notes to expand medical ontologies—think of them as maps of diseases, symptoms, and their relationships. * Zero-shot: works without extra training or labeled data.
AI
AI can be impressively accurate in medicine—but dangerously overconfident when cases are ambiguous. MedBayes‑Lite offers a fix: a lightweight add‑on that helps clinical language models say how sure they are. How it helps, without retraining: * Estimates what the model doesn’t know (epistemic uncertainty) using Monte Carlo
ASR
Not all transcription mistakes are equal. In clinic visits, the go-to score Word Error Rate (WER) can look fine even when errors change care-critical meaning. * Expert clinicians compared true utterances with ASR transcripts in two doctor-patient datasets, labeling the impact: No, Minimal, or Significant. * WER and many other metrics poorly
AI
Words like "bank" can mean a riverbank or a financial institution. Getting the right meaning—called word sense disambiguation—is hard for AI, especially when we need rich, logic-friendly knowledge for reasoning. This paper introduces an annotation-free workflow that teams up symbolic language understanding with large language models:
AI
Teaching chatbots to stop contradicting themselves Ever had a bot say one thing, then the opposite a few turns later? This study introduces DECODE—a new task and dataset for spotting contradictions in everyday conversations, drawn from both human-human and human-bot chats. * New data beats existing natural language inference (NLI)
AI
What if AI could learn to translate without any bilingual textbooks? This research shows how pictures can act as a bridge between languages. When we look at the same photo, speakers of different languages describe the same objects and actions. The team uses images as “pivots” so a model can
MachineLearning
Training AI usually needs lots of human-labeled examples. This work shows a different path: make models learn from plenty of unlabeled data by asking them to stay consistent under strong "noise." Instead of simple tweaks (like small crops or word drops), they use powerful data augmentation: RandAugment for
AI
Can AI essay graders be fooled? New research shows many automatic essay scoring (AES) systems reward “word soups” over real writing. Using interpretability tools, the authors found that a few keywords drive most of the score, while flow, grammar, and coherence matter far less. The wild part: because these models
AI
Why do AI models sometimes sound sure while being wrong? This study spotlights a subtle culprit: spurious correlations—strong but misleading patterns in training data (like linking certain surnames to a nationality). * These shortcuts make LLMs produce confident, wrong answers. * Making models bigger doesn’t fix it. * Popular detectors—confidence
AI
AI personas that actually feel human What if our "AI citizens" behaved less like cardboard cutouts and more like real, diverse people? DeepPersona is a generative engine that builds rich, fully narrated synthetic personas at scale. It first assembles a large, hierarchical taxonomy of human attributes by mining
AI
Meet AdaRec: smarter recommendations from fewer clicks AdaRec is a new way to personalize recommendations using large language models. Instead of hand-crafted features, it turns your past interactions into short, readable stories—“narrative profiles”—and lets an LLM reason over them with just a few examples. It thinks in two