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LLMs & generative AIFact-checking & verification
Atlas: Few-shot Learning with Retrieval Augmented Language ModelsATLAS is a retrieval-augmented language model that achieves strong few-shot performance on knowledge-intensive tasks by jointly training a dense retriever with a sequence-to-sequence language model. Unlike standard language models that must memorize knowledge, ATLAS retrieves relevant documents at both pre-training and fine-tuning…
LLMs & generative AIDetection methods
Discovering Latent Knowledge in Language Models Without SupervisionLanguage models trained with standard objectives (imitation, reward-based) can output false statements even when they internally "know" the truth. This paper proposes Contrast-Consistent Search (CCS), an unsupervised method that exploits truth's logical structure—a statement and its negation cannot both be true—to recover latent…
Datasets & evaluationDetection methods
NELA-GT-2022: A Large Multi-Labelled News Dataset for The Study of Misinformation in News ArticlesPresents the fifth NELA-GT dataset release: 1.78 million news articles from 361 outlets spanning all of 2022, with source-level veracity labels from Media Bias/Fact Check and embedded tweet data. Focuses on stabilizing collection infrastructure to achieve near-complete temporal coverage, enabling robust benchmarking of fake news…
Psychology & interventionsFact-checking & verification
The psychological drivers of misinformation belief and its resistance to correctionThis comprehensive review establishes that misinformation's power lies not in exposure but in how cognitive, social, and affective factors shape belief formation and persist even after correction. False beliefs arise from intuitive thinking, memory failures, emotional content, in-group bias, and source credibility judgments; corrections…