Summary of Adaptive Concept Bottleneck For Foundation Models Under Distribution Shifts, by Jihye Choi et al.
Adaptive Concept Bottleneck for Foundation Models Under Distribution Shifts
by Jihye Choi, Jayaram Raghuram, Yixuan Li, Somesh Jha
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 paper explores the potential of Concept Bottleneck Models (CBMs) in transforming complex, non-interpretable foundation models into interpretable decision-making pipelines. By leveraging high-level concept vectors, CBMs can provide transparent and explainable predictions. The authors focus on test-time deployment of CBM pipelines “in the wild”, where input distributions often shift from the original training distribution. They identify potential failure modes under different types of distribution shifts and propose an adaptive concept bottleneck framework to address these failures using unlabeled data from the target domain. Empirical evaluations show that their adaptation method produces better-aligned interpretations and boosts post-deployment accuracy by up to 28%. The proposed CBM architecture can be applied to various domains, including healthcare, finance, and security. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making big models more understandable. Right now, these models are like black boxes, which makes it hard to know why they make certain decisions. The authors want to change that by creating a new kind of model that can explain its predictions in simple terms. They’re testing this idea with real-world data and finding that it works really well, especially when the input data is different from what the model was trained on. This could be super helpful for fields like healthcare, where doctors need to know why certain treatments are being recommended. |
