Year: 2024
Type: conference-paper
Abstract: Despite significant advancements in natural language generation, controlling language models to produce texts with desired attributes remains a formidable challenge.In this work, we introduce RSA-Control, a training-free controllable text generation framework grounded in pragmatics.RSA-Control directs the generation process by recursively reasoning between imaginary speakers and listeners, enhancing the likelihood that target attributes are correctly interpreted by listeners amidst distractors.Additionally, we introduce a self-adjustable rationality parameter, which allows for automatic adjustment of control strength based on context.Our experiments, conducted with two task types and two types of language models, demonstrate that RSA-Control achieves strong attribute control while maintaining language fluency and content consistency.Our code is available at https://github.com/Ewanwong/RSA-Control.
Authors: Yifan Wang, Vera Demberg
Institution: Saarland University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4404783988