PositionID: LLMs can Control Lengths, Copy and Paste with Explicit
  Positional Awareness
Authors: name: Zekun Wang, name: Feiyu Duan, name: Yibo Zhang, name: Wangchunshu Zhou, name: Ke Xu, name: Wenhao Huang, name: Jie Fu

Large Language Models (LLMs) demonstrate impressive capabilities across
various domains, including role-playing, creative writing, mathematical
reasoning, and coding. Despite these advancements, LLMs still encounter
challenges with length control, frequently failing to adhere to specific length
constraints due to their token-level operations and insufficient training on
data with strict length limitations. We identify this issue as stemming from a
lack of positional awareness and propose novel approaches--PositionID Prompting
and PositionID Fine-Tuning--to address it. These methods enhance the model's
ability to continuously monitor and manage text length during generation.
Additionally, we introduce PositionID CP Prompting to enable LLMs to perform
copy and paste operations accurately. Furthermore, we develop two benchmarks
for evaluating length control and copy-paste abilities. Our experiments
demonstrate that our methods significantly improve the model's adherence to
length constraints and copy-paste accuracy without compromising response
quality.