@inproceedings{yuan-etal-2025-sub,
    title = "From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via {M}arker{G}en",
    author = "Yuan, Peiwen  and
      Tan, Chuyi  and
      Feng, Shaoxiong  and
      Li, Yiwei  and
      Wang, Xinglin  and
      Zhang, Yueqi  and
      Shi, Jiayi  and
      Pan, Boyuan  and
      Hu, Yao  and
      Li, Kan",
    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.850/",
    pages = "17370--17390",
    ISBN = "979-8-89176-251-0",
    abstract = "Despite the rapid progress of large language models (LLMs), their length-controllable text generation (LCTG) ability remains below expectations, posing a major limitation for practical applications. Existing methods mainly focus on end-to-end training to reinforce adherence to length constraints. However, the lack of decomposition and targeted enhancement of LCTG sub-abilities restricts further progress. To bridge this gap, we conduct a bottom-up decomposition of LCTG sub-abilities with human patterns as reference and perform a detailed error analysis. On this basis, we propose MarkerGen, a simple-yet-effective plug-and-play approach that: (1) mitigates LLM fundamental deficiencies via external tool integration; (2) conducts explicit length modeling with dynamically inserted markers; (3) employs a three-stage generation scheme to better align length constraints while maintaining content quality. Comprehensive experiments demonstrate that MarkerGen significantly improves LCTG across various settings, exhibiting outstanding effectiveness and generalizability."
}