UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation

arXiv:2311.15296v2 Announce Type: replace
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 utility in professional contexts. To assess the authentic reliability of LLMs in text generation, numerous initiatives have developed benchmark evaluations for hallucination phenomena. Nevertheless, these …

abstract arxiv benchmarking chinese content generation contributors cs.cl diverse hallucination industries language language models language processing large language large language models llms natural natural language natural language processing pivotal processing professional quality scale statistical type via