@inproceedings{qin-etal-2025-overview,
    title = "Overview of {CCL}25-Eval Task 1: The Fifth Spatial Cognition Evaluation ({S}pa{CE}2025)",
    author = "Qin, Yuhang  and
      Xiao, Liming  and
      Hu, Nan  and
      Deng, Sirui  and
      Ma, Jingyuan  and
      Cui, Hyang  and
      Zhang, Zihan  and
      Tsai, Chi Hsu  and
      Ding, Jinkun  and
      Kang, Sumin  and
      Sui, Zhifang  and
      Zhan, Weidong",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-2.4/",
    pages = "33--46",
    abstract = "``The Fifth Spatial Cognition Evaluation (SpaCE2025) presents a benchmark aimed at evaluating the spatial semantic understanding and reasoning capabilities of Large Language Models(LLMs), primarily in Chinese.It consists of five subtasks: (1) Retrieving Spatial Referents(RSR), (2) Detecting Spatial Semantic Anomalies (DSA), (3) Recognizing Synonymous SpatialExpression (RSE), (4) Spatial Position Reasoning (SPR) in Chinese, and (5) SPR in English. The fourth and fifth subtask share the same content and structure, differing only in language, and are designed to assess the cross-linguistic spatial reasoning capability of LLMs. A total of 12 teams submitted their final results, and the best-performing team achieved an accuracy of 0.7931. The results suggest that while LLMs are capable of handling basic spatial semantic understanding tasks such as RSR, their performance on more complex tasks, such as DSA and RSE, still re-quires improvement. Additionally, finetuning methods that effectively activate LLMs' reasoning ability are essential to improve their performance.''"
}