Papers by Yezhaohui Wang

1 papers
UHGEval: Benchmarking the Hallucination of Chinese Large Language Models via Unconstrained Generation (2024.acl-long)

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Challenge: Large language models (LLMs) produce hallucinated text, compromising their practical utility in professional contexts.
Approach: They have developed an unconstrained hallucination generation evaluation benchmark that contains hallucines generated by large language models with minimal restrictions.
Outcome: The proposed benchmarks are based on a Chinese-language dataset that is lacking in the field.

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