ACL 2025long0 citations

From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via MarkerGen

Peiwen Yuan, Chuyi Tan, Shaoxiong Feng, Yiwei Li, Xinglin Wang, Yueqi Zhang, Jiayi Shi, Boyuan Pan

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.

BibTeX
@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://aclanthology.org/2025.acl-long.850/",
    doi = "10.18653/v1/2025.acl-long.850",
    pages = "17370--17390",
    ISBN = "979-8-89176-251-0"
}
From Sub-Ability Diagnosis to Human-Aligned Generation: Bridging the Gap for Text Length Control via MarkerGen · ACL 2025