Summary of Locate&edit: Energy-based Text Editing For Efficient, Flexible, and Faithful Controlled Text Generation, by Hye Ryung Son and Jay-yoon Lee
Locate&Edit: Energy-based Text Editing for Efficient, Flexible, and Faithful Controlled Text Generation
by Hye Ryung Son, Jay-Yoon Lee
First submitted to arxiv on: 30 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 The proposed Locate&Edit (L&E) approach is an efficient and flexible method for controlled text generation (CTG), which edits text outputs from a base language model (LM) using off-the-shelf energy models. This method locates spans relevant to constraints, such as toxicity, and then replaces them with more suitable alternatives while preserving the core semantics of the original generations. L&E is compatible with black-box LMs and doesn’t require specific architecture for its component models, making it versatile. The approach achieves superior semantic preservation of base LM generations and speed, while obtaining competitive or improved constraint satisfaction. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary L&E is a new way to control what language models say. Right now, most methods only work with simple models and don’t do a great job keeping the original meaning. L&E fixes this by finding parts of the text that need changing and replacing them with better options. It works well even with complex black-box models and can use different energy models to make changes. The results show that L&E is fast, keeps the original meaning, and does a good job following rules. |
Keywords
» Artificial intelligence » Language model » Semantics » Text generation
