RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912085211873280 |
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| author | Wang, Yifan Demberg, Vera |
| author_facet | Wang, Yifan Demberg, Vera |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_19109 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework Wang, Yifan Demberg, Vera Artificial Intelligence Computation and Language 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. |
| title | RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework |
| topic | Artificial Intelligence Computation and Language |
| url | https://arxiv.org/abs/2410.19109 |