@inproceedings{hayati-etal-2026-rubrics,
    title = "From Rubrics to Recipe: Principle-Centric Benchmark for Evaluating Large Language Models",
    author = "Hayati, Shirley Anugrah  and
      Wang, Ruizi  and
      Kang, Dongyeop",
    editor = "Akhtar, Mubashara  and
      Batzner, Jan  and
      Choshen, Leshem  and
      Ghosh, Avijit  and
      Gohar, Usman  and
      Mickel, Jennifer  and
      Pant, Ichhya  and
      Talat, Zeerak  and
      Lin, Michelle",
    booktitle = "Proceedings of the Workshop on Evaluating Evaluations ({E}val{E}val)",
    month = jul,
    year = "2026",
    address = "San Diego, CA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl-workshops/2026.evaleval-1.15/",
    pages = "82--99",
    ISBN = "979-8-89176-429-3",
    abstract = "Large language models (LLMs) are often evaluated on benchmarks that rely on surfacelevel instructions, obscuring what defines highquality performance. We argue that tasks can be more precisely characterized through principles: human-readable rules that specify what matters for a good response to the task. Our study proposes a framework to automatically extract and generate task-level principles for data generation and evaluation. Using this approach, we build a benchmark of over 20K principle-aligned instances, enabling controllable data creation and fine-grained, interpretable assessment of LLMs. Experiments show that principles both improve output quality and scale evaluation beyond manual curation, offering a new recipe for principled assessment of LLM capabilities.1"
}