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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · The Decoder English(EN) ·

    AI models often give the right answers but point to the wrong sources

    Leading AI models such as GPT and Gemini frequently provide correct answers while citing non-existent or irrelevant evidence. This phenomenon, termed "attribution hallucination" by researchers at Peking University, poses a significant risk in critical sectors like law and medicine. To address this, a new benchmark called CiteVQA has been developed to systematically evaluate and identify these citation errors. AI

    AI models often give the right answers but point to the wrong sources

    IMPACT New benchmark CiteVQA highlights attribution hallucination in AI models, posing risks for regulated industries and prompting development of more reliable citation methods.

  2. TOOL · Mastodon — sigmoid.social Polski(PL) ·

    New CiteVQA study reveals leading AI models, including GPT-4, often provide correct answers but fail to reliably cite their sources, raising concerns

    A new study from CiteVQA indicates that leading AI models, including GPT-4, frequently provide correct answers but struggle to reliably cite their sources. This inability to attribute information accurately raises concerns about the trustworthiness and verifiability of AI-generated content. The research highlights a critical gap in current AI capabilities, particularly in applications requiring factual accuracy and source transparency. AI

    IMPACT Highlights a critical gap in AI's ability to provide verifiable information, impacting trust and reliability in AI-generated content.