Language Model Sentence Completion with a Parser-Driven Rhetorical
  Control Method
Joshua Zingale Jugal Kalita
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.