Abstract
Controlled text generation (CTG) seeks to guide large language model (LLM) output, that statistical language generation would conform 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.- Anthology ID:
- 2024.eacl-short.18
- Volume:
- Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers)
- Month:
- March
- Year:
- 2024
- Address:
- St. Julian’s, Malta
- Editors:
- Yvette Graham, Matthew Purver
- Venue:
- EACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 193–203
- Language:
- URL:
- https://aclanthology.org/2024.eacl-short.18
- DOI:
- Cite (ACL):
- Joshua Zingale and Jugal Kalita. 2024. Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method. In Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 2: Short Papers), pages 193–203, St. Julian’s, Malta. Association for Computational Linguistics.
- Cite (Informal):
- Language Model Sentence Completion with a Parser-Driven Rhetorical Control Method (Zingale & Kalita, EACL 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.eacl-short.18.pdf