CMNIE is a new benchmark for extracting structured information from Chinese military news, covering event triggers, arguments, named entities, and entity relations under a unified schema. The dataset contains 13,000 manually annotated instances with 7 event types, 10 argument roles, 7 entity types, and 8 relation types. Experiments show that current supervised models, zero‑shot LLMs, and fine‑tuned LLMs struggle with relation extraction and exact span matching, highlighting the challenge of joint structured extraction in this domain.
By Yan Yu, Mengna Zhu, Zhenyu Song, Hao Yang, Haiwen Chen, Mao Wang
arXiv:2510. 06039v2 Announce Type: replace-cross Abstract: Reliable evaluation of knowledge-grounded Large Language Models (LLMs) in Chinese requires resources that explicitly align Chinese-language text with verifiable Knowledge Graph (KG) facts.
By Chengwei Wu, Xingrui Zhuo, Mingyang Gao, Xinghe Cheng, Zhichao Yan, Jiapu Wang
arXiv:2608.29133v1 Announce Type: new
Abstract: History is not preserved in complete, continuous form. Accounts of a person's activities, relationships and historical contexts are scattered across te...
By Yifeng Lu, Zijie Yang, Jie Li, Qingkai Min, Yue Zhang
Was this person ever at that place, and if so, when? Answering such questions from noisy, multilingual historical documents is the central challenge of HIPE-2026, the third edition of the HIPE evaluation series.
CNeo-Bench is a new benchmark comprising 4,759 Chinese neologisms, each with reference definitions and categorized by linguistic mechanisms such as phonetic substitution and visual character decomposition. The benchmark includes a two-tier evaluation framework that tests whether models can describe a neologism and whether they can manipulate its underlying mechanism. Evaluation of 18 large language models shows that most perform poorly on definition generation (below 40%) and exhibit a recognition‑manipulation gap, often paraphrasing rather than restoring the original form; few‑shot prompting helps but does not fully resolve the errors.
By Kaiyan Zhao, Zhongtao Miao, Zheyong Xie, Shaosheng Cao, Yoshimasa Tsuruoka
arXiv:2608. 10970v1 Announce Type: cross Abstract: Recent advances in Large Language Models (LLMs) have demonstrated strong capabilities in generating semantically relevant concepts and relations, making them promising tools for taxonomy enrichment.
By Zeinab Ghamlouch, Mehwish Alam