Ruler: A Model-Agnostic Method to Control Generated Length for Large
  Language Models
Authors: name: Jiaming Li, name: Lei Zhang, name: Yunshui Li, name: Ziqiang Liu, name: yuelin bai, name: Run Luo, name: Longze Chen, name: Min Yang

The instruction-following ability of large language models enables humans to
interact with AI agents in a natural way. However, when required to generate
responses of a specific length, large language models often struggle to meet
users' needs due to their inherent difficulty in accurately perceiving
numerical constraints. To explore the ability of large language models to
control the length of generated responses, we propose the Target Length
Generation Task (TLG) and design two metrics, Precise Match (PM) and Flexible
Match (FM) to evaluate the model's performance in adhering to specified
response lengths. Furthermore, we introduce a novel, model-agnostic approach
called Ruler, which employs Meta Length Tokens (MLTs) to enhance the
instruction-following ability of large language models under length-constrained
instructions. Specifically, Ruler equips LLMs with the ability to generate
responses of a specified length based on length constraints within the
instructions. Moreover, Ruler can automatically generate appropriate MLT when
length constraints are not explicitly provided, demonstrating excellent
versatility and generalization. Comprehensive experiments show the
effectiveness of Ruler across different LLMs on Target Length Generation Task,
e.g., at All Level 27.97 average gain on PM, 29.57 average gain on FM. In
addition, we conduct extensive ablation experiments to further substantiate the
efficacy and generalization of Ruler. Our code and data is available at
https://github.com/Geaming2002/Ruler.