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Controllable text generation is a fundamental aspect of natural languagegeneration, with numerous methods proposed for different constraint types.However, these approaches often require significant architectural or decodingmodifications, making them challenging to apply to additional constraints orresolve different constraint combinations. To address this, our paperintroduces Regular Expression Instruction (REI), which utilizes aninstruction-based mechanism to fully exploit regular expressions' advantages touniformly model diverse constraints. Specifically, our REI supports all popularfine-grained controllable generation constraints, i.e., lexical, positional,and length, as well as their complex combinations, via regular expression-styleinstructions. Our method only requires fine-tuning on medium-scale languagemodels or few-shot, in-context learning on large language models, and requiresno further adjustment when applied to various constraint combinations.Experiments demonstrate that our straightforward approach yields high successrates and adaptability to various constraints while maintaining competitivenessin automatic metrics and outperforming most previous baselines.

Toward Unified Controllable Text Generation via Regular Expression Instruction