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

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More and more young people are choosing the agricultural profession, and...

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Top Five Chinese EV startups: Li Auto Leads and Xiaomi Gaining...

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The smart glasses can be purchased for as little as $295...

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ChatGPT continues its dominance, but this Google AI Tool is gaining...

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The Download: Google Project Astra and China’s Export Bans

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Altman admits that ChatGPT Pro is struggling to make a profit...

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AI Hardware is in its ‘Put up or Shut Up Era’

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Nvidia’s RTX-5090 with 32GB GDDR7 Memory

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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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A Step-by-Step Guide to Implement Intelligent Request Routing with Claude

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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...