AI
TL;DR An open-source platform helps creators check if their content was used to train large language models, without heavy compute or opaque workflows. What it does * Verifies if a specific work appears in LLM training datasets. * Improves similarity matching and dataset validation. * Cuts compute by 10-30% via efficient API
AI
If you’re a writer, artist, or publisher, you can now verify whether your content was used to train large language models—without a data center or a PhD. In “Copyright Detection in Large Language Models,” David Szczecina, Senan Gaffori, and Edmond Li introduce an open-source platform that makes copyright
AI
Look me in the eye, camera. Ever snapped a great photo except your eyes aren’t on the camera? This research introduces GazeCorrection, an AI that subtly redirects your gaze by re-synthesizing just the eye region—keeping your identity and expression intact. How it works: a self-supervised generative model treats
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
Researchers asked a simple question: given just your Big Five personality answers, can an AI “role‑play” how you’d score on other psychology questionnaires? Using responses from 816 people, large language models were prompted to generate answers on nine additional psychological scales. The patterns in the AI’s generated
AI
If your AI segments tumors, who is it serving—and who might it be failing? A new audit of the MAMA-MIA breast cancer segmentation dataset examines fairness not in classification, but in segmentation—the step that often feeds every downstream decision. * Researchers evaluated automated segmentation quality by age, ethnicity, and