{"ID":2865507,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.02337","arxiv_id":"2510.02337","title":"CRACQ: A Multi-Dimensional Approach To Automated Document Assessment","abstract":"This paper presents CRACQ, a multi-dimensional evaluation framework tailored to evaluate documents across f i v e specific traits: Coherence, Rigor, Appropriateness, Completeness, and Quality. Building on insights from traitbased Automated Essay Scoring (AES), CRACQ expands its fo-cus beyond essays to encompass diverse forms of machine-generated text, providing a rubricdriven and interpretable methodology for automated evaluation. Unlike singlescore approaches, CRACQ integrates linguistic, semantic, and structural signals into a cumulative assessment, enabling both holistic and trait-level analysis. Trained on 500 synthetic grant pro-posals, CRACQ was benchmarked against an LLM-as-a-judge and further tested on both strong and weak real applications. Preliminary results in-dicate that CRACQ produces more stable and interpretable trait-level judgments than direct LLM evaluation, though challenges in reliability and domain scope remain","short_abstract":"This paper presents CRACQ, a multi-dimensional evaluation framework tailored to evaluate documents across f i v e specific traits: Coherence, Rigor, Appropriateness, Completeness, and Quality. Building on insights from traitbased Automated Essay Scoring (AES), CRACQ expands its fo-cus beyond essays to encompass diverse...","url_abs":"https://arxiv.org/abs/2510.02337","url_pdf":"https://arxiv.org/pdf/2510.02337v1","authors":"[\"Ishak Soltani\",\"Francisco Belo\",\"Bernardo Tavares\"]","published":"2025-09-26T17:01:54Z","proceeding":"cs.CL","tasks":"[\"cs.CL\",\"cs.AI\",\"cs.LG\"]","methods":"[\"Large Language Model\"]","has_code":false}