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AI tool uses face photos to estimate biological age and predict...

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Pebble Founder Demos Pebble Core 2 Duo Smartwatch: ChatGPT Integration Next?

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OpenAI yanked a ChatGPT update. Here’s what it said and why...

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Perplexity builds a browser to collect data on customers for targeted...

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Instagram co-founder warns that AI chatbots are not useful and instead...

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Amazon CEO says 100,000 users now have Alexa+

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Fears confirmed! Rockstar announces Grand Theft Auto VI Release Date

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Apple posts highest ever Services revenue

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Anthropic

Huawei Pura X is disassembled in this video

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Is Automated Hallucination Detection in LLMs Feasible? A Theoretical and Empirical...

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This AI Paper Introduce WebThinker: A Deep Research Agent that Empowers...

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Researchers from Fudan University Introduce Lorsa: A Sparse Attention Mechanism That...

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Is Automated Hallucination Detection in LLMs Feasible? A Theoretical and Empirical...

Recent advancements in LLMs have significantly improved natural language understanding, reasoning, and generation. These models now excel at diverse tasks like mathematical problem-solving and generating contextually appropriate text. However, a persistent challenge remains: LLMs often generate hallucinations—fluent but factually incorrect responses. These hallucinations undermine the reliability of LLMs, especially...