Controlled text generation (CTG) seeks to guide large language model (LLM) output to produce text that conforms to desired criteria. The current study presents a novel CTG algorithm that enforces adherence toward specific rhetorical relations in an LLM sentence-completion context by a parser-driven decoding scheme that requires no model fine-tuning. The method is validated both with automatic and human evaluation. The code is accessible on GitHub.

翻译：受控文本生成（CTG）旨在引导大型语言模型（LLM）输出符合特定标准的文本。本研究提出了一种新型CTG算法，通过无需模型微调的解析器驱动解码方案，在LLM句子补全语境中强制遵循特定修辞关系。该方法通过自动评估与人工评估双重验证。相关代码可在GitHub上获取。