Abstract: The rapid advancement of Large Language Models (LLMs) has facilitated remarkable progress in natural language generation. Nevertheless, a major challenge remains, and this is the problem of hallucinations, where the models generate outputs that are linguistically valid but factually incorrect, logically unjustified, and even completely fabricated. The consequences of undetected hallucinations are severe, spanning patient safety risks in healthcare, fabricated legal precedents in judicial contexts, and distorted scholarly discourse in scientific research. Although.....
Key Word: Hallucination Detection, Deep Learning, Large Language Model, Model Reliability, AI Safety
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