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Uhgeval: Benchmarking The Hallucination Of Chinese Large Language Models Via Unconstrained Generation

Abstract

Large language models (LLMs) have emerged as pivotal contributors in contemporary natural language processing and are increasingly being applied across a diverse range of industries. However, these large-scale probabilistic statistical models cannot currently ensure the requisite quality in professional content generation. These models often produce hallucinated text, compromising their practical

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