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

TL;DR
RSA-Control is a training-free framework for controllable text generation that uses pragmatic reasoning between speakers and listeners, with an adjustable control strength, to produce attribute-controlled, fluent, and consistent texts.
Contribution
It introduces a novel pragmatics-grounded, training-free approach with an adjustable parameter for improved controllable text generation.
Findings
Achieves strong attribute control in generated texts.
Maintains language fluency and content consistency.
Effective across different task types and language models.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
Taxonomy
TopicsNatural Language Processing Techniques
