Summary of Multi-attribute Constraint Satisfaction Via Language Model Rewriting, by Ashutosh Baheti et al.
Multi-Attribute Constraint Satisfaction via Language Model Rewriting
by Ashutosh Baheti, Debanjana Chakraborty, Faeze Brahman, Ronan Le Bras, Ximing Lu, Nouha Dziri, Yejin Choi, Mark Riedl, Maarten Sap
First submitted to arxiv on: 26 Dec 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 Multi-Attribute Constraint Satisfaction (MACS) method enables fine-grained control over large language models on sequential domains to satisfy user-specified constraints on multiple external attributes. This generalized approach trains language models as editors by sampling diverse multi-attribute edit pairs from an initial set of paraphrased outputs. During inference, the model iteratively improves upon its previous solution to satisfy constraints for all attributes using a designed constraint satisfaction reward. The MACS method outperforms strong domain-specific baselines on two challenging tasks: Text Style Transfer and Protein Design. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you have a big box of LEGOs, and you want to build something specific with certain colors and shapes. This paper helps create a new way for computers to control language models, like building with LEGOs, but for words! The computer can specify what kind of text it wants to generate, like making a sentence more positive or changing the complexity. This is useful because current methods are not very good at controlling multiple attributes at once. The researchers created a new method called MACS that can do this and tested it on two different tasks: one about rewriting text and another about designing proteins. They found that their method worked better than other approaches for these tasks. |
Keywords
» Artificial intelligence » Inference » Style transfer
