Summary of Rsa-control: a Pragmatics-grounded Lightweight Controllable Text Generation Framework, by Yifan Wang et al.
RSA-Control: A Pragmatics-Grounded Lightweight Controllable Text Generation Framework
by Yifan Wang, Vera Demberg
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel framework called RSA-Control is introduced for training-free controllable text generation, addressing the challenge of producing texts with desired attributes. The approach uses pragmatics and recursive reasoning to direct the generation process, enhancing the likelihood that target attributes are correctly interpreted by listeners amidst distractors. The framework also features a self-adjustable rationality parameter, allowing for automatic adjustment of control strength based on context. Experiments demonstrate strong attribute control while maintaining language fluency and content consistency, with RSA-Control achieving promising results across two task types and two language model types. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper introduces a new way to control language models so they produce texts that meet certain requirements. The approach uses a type of reasoning called pragmatics to make sure the text has the right attributes. It’s like having a conversation where you take turns saying things, and each person understands what the other is trying to say. This framework also adjusts its level of control based on the context. The results show that this method works well for producing texts with specific features while still being natural-sounding. |
Keywords
» Artificial intelligence » Language model » Likelihood » Text generation
