@inproceedings{saaro-etal-2026-classify,
    title = "Do {NOT} Classify and Count: Hybrid Attribute Control Success Evaluation",
    author = {Saaro, Felix Matthias  and
      von D{\"a}niken, Pius  and
      Cieliebak, Mark  and
      Deriu, Jan Milan},
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Proceedings of the 19th Conference of the {E}uropean Chapter of the {A}ssociation for {C}omputational {L}inguistics (Volume 1: Long Papers)",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.eacl-long.48/",
    pages = "1101--1114",
    ISBN = "979-8-89176-380-7",
    abstract = "Evaluating attribute control success in controllable text generation and related generation tasks typically relies on pretrained classifiers. We show that this widely used classify-and-count approach yields biased and inconsistent results, with estimates varying significantly across classifiers. We frame control success estimation as a quantification task and apply a hybrid Bayesian method that combines classifier predictions with a small number of human labels for calibration. To test our approach, we collected a two-modality test dataset consisting of 600 human-rated samples and 60,000 automatically rated samples. Our experiments show that our approach produces robust estimates of control success across both text and text-to-image generation tasks, offering a principled alternative to current evaluation practices."
}