@inproceedings{guo-etal-2025-critiq,
    title = "{C}riti{Q}: Mining Data Quality Criteria from Human Preferences",
    author = "Guo, Honglin  and
      Lv, Kai  and
      Guo, Qipeng  and
      Liang, Tianyi  and
      Xi, Zhiheng  and
      Song, Demin  and
      Zhang, Qiuyinzhe  and
      Sun, Yu  and
      Chen, Kai  and
      Qiu, Xipeng  and
      Gui, Tao",
    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.792/",
    pages = "16240--16261",
    ISBN = "979-8-89176-251-0",
    abstract = "Language model heavily depends on high-quality data for optimal performance. Existing approaches rely on manually designed heuristics, the perplexity of existing models, training classifiers, orcareful prompt engineering, which require significant expert experience and human annotation effort while introduce biases. We introduce CritiQ, a novel data selection method that automatically mines criteria from human preferences for data quality with only {\textasciitilde}30 human-annotated pairs and performs efficient data selection. The main component, CritiQ Flow, employs a manager agent to evolve quality criteria and worker agents to make pairwise judgments. We build a knowledge base that extracts quality criteria from previous work to boost CritiQ Flow. Compared to perplexity- and classifier-based methods, verbal criteria are more interpretable and have greater reusable value. After deriving the criteria, we train the CritiQ Scorer to give quality scores and perform efficient data selection. We demonstrate the effectiveness of our method in the code, math, and logic domains, achieving high accuracy on human-annotated test sets. To validate the quality of the selected data, we continually train Llama 3.2 models and observe improved performance on downstream tasks compared to uniform sampling. Ablation studies validate the benefits of the knowledge base and the reflection process. We analyze how criteria evolve and the effectiveness of majority voting."
}