RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework
Yifan Wang, Vera Demberg · 2024
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.