  Controllable text generation is a fundamental aspect of natural language
generation, with numerous methods proposed for different constraint types.
However, these approaches often require significant architectural or decoding
modifications, making them challenging to apply to additional constraints or
resolve different constraint combinations. To address this, our paper
introduces Regular Expression Instruction (REI), which utilizes an
instruction-based mechanism to fully exploit regular expressions' advantages to
uniformly model diverse constraints. Specifically, our REI supports all popular
fine-grained controllable generation constraints, i.e., lexical, positional,
and length, as well as their complex combinations, via regular expression-style
instructions. Our method only requires fine-tuning on medium-scale language
models or few-shot, in-context learning on large language models, and requires
no further adjustment when applied to various constraint combinations.
Experiments demonstrate that our straightforward approach yields high success
rates and adaptability to various constraints while maintaining competitiveness
in automatic metrics and outperforming most previous baselines.
