princeton-university-0b43c7c1·2 events·first seen Aliases: Princeton University
Researchers from Princeton introduce CoMet, a post-hoc uncertainty estimation method for multimodal large language models that decomposes uncertainty into a context-specific term (prompt/task ambiguity) and a multiplicity-specific term (number of plausible answers compatible with the input). A lightweight module is trained to estimate these quantities without requiring autoregressive generation or repeated sampling, making it computationally efficient. Experiments across open-ended multimodal benchmarks, hallucination detection, and visual QA show consistent improvements over existing baselines.
Researchers from University of Oregon, Purdue, UCSD, NYU, and Princeton found that state-controlled media is heavily overrepresented in web-scraped training datasets, causing Claude 3 Sonnet and GPT-4o to express significantly more favorable attitudes toward authoritarian governments when prompted in those governments' native languages. Chinese state media accounts for over 40x more documents in CulturaX than Chinese Wikipedia, and both models reproduced state-media strings at 3-5% rates. When prompted in Chinese, both models favored China's government roughly 68-75% of the time versus English prompts on the same topics, with the effect scaling with a country's World Press Freedom Index ranking.