Summary of Aligning Model Properties Via Conformal Risk Control, by William Overman et al.
Aligning Model Properties via Conformal Risk Control
by William Overman, Jacqueline Jil Vallon, Mohsen Bayati
First submitted to arxiv on: 26 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 approach to address misalignment in machine learning models is proposed in this paper. The authors recognize that traditional methods for aligning models with end-user requirements are limited to generative AI settings and often require costly retraining processes. Instead, they introduce property testing as a means to interpret model alignment through defining a subset of functions that exhibit desired behaviors. A procedure is developed for converting queries for testing these properties into loss functions suitable for conformal risk control. The authors provide probabilistic guarantees for the resulting conformal interval and demonstrate applications on supervised learning datasets for shape-constrained properties like monotonicity and concavity. This methodology is flexible and can be applied to various desired properties, emphasizing the importance of model alignment techniques even as model sizes or training data increase. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning models need to align with human requirements. Researchers tried using human feedback, but it only works for certain types of AI. For other kinds of AI, like those that give numerical answers, we need a new approach. This paper proposes an alternative strategy called property testing. It helps identify when a model is not aligned and corrects it. The authors develop a method to convert test queries into loss functions suitable for conformal risk control. They show this works on real datasets and can be used for different properties. Even with bigger models or more training data, we still need these alignment techniques. |
Keywords
» Artificial intelligence » Alignment » Machine learning » Supervised
