ULTIMATE Fact Checking AI (Johns Hopkins, Stanford) скачать в хорошем качестве

ULTIMATE Fact Checking AI (Johns Hopkins, Stanford)

As large language models (LLMs) increasingly automate high-stakes tasks like clinical documentation, their propensity for factual inaccuracies—omissions, hallucinations, and contextual ambiguities—poses critical risks. Employing novel methodological frameworks to quantify error propagation and semantic coherence, the work lays bare the inadequacies of current evaluation paradigms while hinting at transformative strategies to align AI-generated claims with ground-truth evidence. For those invested in the reliability of automated systems, these papers offer a masterclass in diagnosing—and ultimately resolving—the fragile relationship between language models and factual integrity. All rights w/ authors: Assessing the Limitations of Large Language Models in Clinical Fact Decomposition Monica Munnangi, Akshay Swaminathan, Jason Alan Fries, Jenelle Jindal, Sanjana Narayanan, Ivan Lopez, Lucia Tu, Philip Chung, Jesutofunmi A. Omiye, Mehr Kashyap, Nigam Shah Khoury College of Computer Sciences, Northeastern University; Center for Biomedical Informatics Research, Stanford University; Department of Biomedical Data Science; Stanford Health Care; Department of Medicine; Clinical Excellence Research Center; Department of Anesthesiology, Perioperative & Pain Medicine; Department of Dermatology; Technology and Digital Solutions, Stanford Health Care #airesearch #factcheckers #clinical #aiagents #stanford