@inproceedings{zhang-etal-2026-bloom,
    title = "Bloom-Eval: A Hierarchical Evaluation Benchmark for Automatic Survey Generation Based on Bloom{'}s Taxonomy",
    author = "Zhang, Fei  and
      Zhao, Zhe  and
      Wen, HaiBin  and
      Wei, Tianshuo  and
      Zhang, Zaixi  and
      Yang, Chao  and
      Wei, Ye",
    editor = "Liakata, Maria  and
      Moreira, Viviane P.  and
      Zhang, Jiajun  and
      Jurgens, David",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-long.1315/",
    pages = "28512--28544",
    ISBN = "979-8-89176-390-6",
    abstract = "The rapid advance of automatic survey generation (ASG) has created a critical evaluation challenge. Existing evaluation methods suffer from both cognitive dimensional simplification and methodological unreliability, primarily due to the over-reliance on the ``LLM-as-a-Judge'' approach. To bridge this gap, we establish Bloom-Eval, a six-tiered benchmark based on Bloom{'}s Taxonomy that reliably evaluates ASG systems by prioritizing deterministic algorithms and introducing our GRADE approach for abstract abilities. Furthermore, we construct a large-scale, cross-disciplinary dataset of over 3,000 high-quality papers. Our empirical study on this benchmark reveals that while leading ASG systems are proficient format organizers, they remain unqualified knowledge integrators. This work aims to redefine ASG evaluation standards, shifting the research focus from the formal mimicry of surface structure to the cognitive deepening of intellectual content. Our method provides the ASG field with a systematic, reproducible, and theoretically grounded benchmark to guide future research."
}