mlsanityData to 14 Oct 2026

Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method

arXiv 2402.06125 ↗First posted 9 Feb 2024cs.CLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 9 of its 23 references that are in the map. First posted on arXiv 9 Feb 2024.

Builds on

9 of 23 references.

References · 9

  1. 2019The Curious Case of Neural Text Degenerationcited by 2,916
  2. 2022BLOOM: A 176B-Parameter Open-Access Multilingual Language Modelcited by 1,984
  3. 2019Plug and Play Language Models: A Simple Approach to Controlled Text Generationcited by 799
  4. 2023AI-Generated Content (AIGC): A Surveycited by 59
  5. 2020Exploring Controllable Text Generation Techniquescited by 55
  6. 2021DMRST: A Joint Framework for Document-Level Multilingual RST Discourse Segmentation and Parsingcited by 28
  7. 2021Think Before You Speak: Explicitly Generating Implicit Commonsense Knowledge for Response Generationcited by 26
  8. 2020Multilingual Neural RST Discourse Parsingcited by 13
  9. 2022Plug-and-Play Recipe Generation with Content Planningcited by 5

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Cited by year

Citing papers per year: 2024: 1; 2025: 2.

3 papers.

  1. 2025Speeding up Speculative Decoding via Sequential Approximate Verificationcited by 11
  2. 2024CBF-LLM: Safe Control for LLM Alignmentcited by 9
  3. 2025Control Barrier Function for Aligning Large Language Modelscited by 0