2026-10-10T17:08:58Z https://www.scidb.cn/oai

10.57760/sciencedb.j00133.00690 2026-06-29T08:56:41Z

2026-06-29 CC-Bench doi:10.57760/sciencedb.j00133.00690 en This dataset (CC-Bench) is a multilingual parallel benchmark designed to probe the knowledge boundaries of large language models. It integrates the multilingual alignment features of the MKQA dataset with the high-quality, human-annotated question-answering pairs from the TyDi QA dataset, utilizing its "unanswerable" labels to distinguish between known and unknown question sets. The benchmark comprises 10,000 cross-domain evaluation samples spanning fields such as sports, history, and basic medicine, and is further categorized into factual and text-based subsets. It is primarily used to quantify cross-lingual semantic stability, supporting cognitive boundary detection, retrieval-augmented gating optimization, and hallucination mitigation in black-box scenarios. Cross-Lingual Consistency; Large Language Models; Knowledge Boundary; Hallucination Mitigation shensi Li Wanbin Yang Fan Zhao Xue PUBLIC https://creativecommons.org/licenses/by-nc-nd/4.0/ dataset Science Data Bank