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.

本研究解决了当前大型语言模型在文本长度可控生成方面的不足，主要通过底层分解和针对性增强子能力来改善现状。提出的MarkerGen方法集成了外部工具并动态插入标记，有效提升了文本生成的一致性与质量，实验结果表明其在多个设置下显著改善了长度控制能力。

从子能力诊断到人类对齐生成：通过MARKERGEN弥合文本长度控制的差距