@inproceedings{galvan-sosa-etal-2025-rubriks,
    title = "Rubrik{'}s Cube: Testing a New Rubric for Evaluating Explanations on the {CUBE} dataset",
    author = "Galvan-Sosa, Diana  and
      Gaudeau, Gabrielle  and
      Kavumba, Pride  and
      Li, Yunmeng  and
      Gu, Hongyi  and
      Yuan, Zheng  and
      Sakaguchi, Keisuke  and
      Buttery, Paula",
    editor = "Che, Wanxiang  and
      Nabende, Joyce  and
      Shutova, Ekaterina  and
      Pilehvar, Mohammad Taher",
    booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
    month = jul,
    year = "2025",
    address = "Vienna, Austria",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingestion-acl-25/2025.acl-long.1160/",
    pages = "23800--23839",
    ISBN = "979-8-89176-251-0",
    abstract = "The performance and usability of Large-Language Models (LLMs) are driving their use in explanation generation tasks. However, despite their widespread adoption, LLM explanations have been found to be unreliable, making it difficult for users to distinguish good from bad explanations. To address this issue, we present Rubrik{'}s CUBE{--}an education-inspired rubric and a dataset of 26k explanations, written and later quality-annotated using the rubric by both humans and six open- and closed-source LLMs. The CUBE dataset focuses on two reasoning and two language tasks, providing the necessary diversity for us to effectively test our proposed rubric. Using Rubrik, we find that explanations are influenced by both task and perceived difficulty. Low quality stems primarily from a lack of conciseness in LLM-generated explanations, rather than cohesion and word choice. The full dataset, rubric, and code are available at https://github.com/RubriksCube/rubriks{\_}cube."
}