“The Fourth Chinese Spatial Cognition Evaluation Task (SpaCE 2024) presents the first comprehensive Chinese benchmark to assess spatial semantic understanding and reasoning capabilities of Large Language Models (LLMs). It comprises five subtasks in the form of multiple-choice questions: (1) identifying spatial semantic roles; (2) retrieving spatial referents; (3) detecting spatial semantic anomalies; (4) recognizing synonymous spatial expression with different forms; (5) conducting spatial position reasoning. In addition to proposing new tasks, SpaCE 2024 applied a rule-based method to generate high-quality synthetic data with difficulty levels for the reasoning task. 12 teams submitted their models and results, and the top-performing team attained an accuracy of 60.24%, suggesting that there is still significant room for current LLMs to improve, especially in tasks requiring high spatial cognitive processing.”

“第二届中文空间语义理解评测任务(SpaCE2022)旨在测试机器的空间语义理解能力,包括三个子任务:(1)中文空间语义正误判断任务;(2)中文空间语义异常归因与异常文本识别任务;(3)中文空间实体识别与空间方位关系标注任务。本文围绕SpaCE2022数据集介绍了标注规范和数据集制作流程,总结了改善数据集质量的方法,包括构建STEP标注体系,规范描述空间语义信息;基于语言学知识生成空间异常句子,提高数据多样性;采取双人标注、基于规则的实时质检、人工抽样审核等方式加强数据质量控制;分级管理标注数据,优选高质量数据进入数据集。通过考察数据集分布情况以及机器表现和人类表现,本文发现SpaCE2022数据集的标签分布存在明显偏差,而且正误判断任务和异常归因任务的主观性强,一致性低,这些问题有待在将来的SpaCE任务设计中做进一步优化。”