SciCUEval: A Comprehensive Dataset for Evaluating Scientific Context Understanding in Large Language Models
Year: 2025
Type: preprint
Abstract: Large Language Models (LLMs) have shown impressive capabilities in contextual understanding and reasoning. However, evaluating their performance across diverse scientific domains remains underexplored, as existing benchmarks primarily focus on general domains and fail to capture the intricate complexity of scientific data. To bridge this gap, we construct SciCUEval, a comprehensive benchmark dataset tailored to assess the scientific context understanding capability of LLMs. It comprises ten domain-specific sub-datasets spanning biology, chemistry, physics, biomedicine, and materials science, integrating diverse data modalities including structured tables, knowledge graphs, and unstructured texts. SciCUEval systematically evaluates four core competencies: Relevant information identification, Information-absence detection, Multi-source information integration, and Context-aware inference, through a variety of question formats. We conduct extensive evaluations of state-of-the-art LLMs on SciCUEval, providing a fine-grained analysis of their strengths and limitations in scientific context understanding, and offering valuable insights for the future development of scientific-domain LLMs.
Source: arXiv (Cornell University)
Authors: Yu, Jing, Tang, Yuqi, Feng, Kehua, Rao, Mingyang, Liang, Lei, Zhang, Zhiqiang, Sun, Mengshu, Zhang, Wen, Zhang, Qiang, Ding, Keyan, Chen, Huajun
Institutions: Zhejiang University, Ant Group (China)
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4415328390
Cites:
Cited by:
Related to: