arXiv:2606. 04326v1 Announce Type: cross Abstract: Concept bottleneck models predict outcomes from high-level concepts detected in inputs.
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Concept bottleneck models predict outcomes from high-level concepts detected in inputs. Although concepts provide a simple way to reap benefits from interpretability, very few datasets include concept labels.
The paper introduces a method for efficiently exploring the Rashomon set of Concept Bottleneck Models (CBMs) by using a parallel parameter‑efficient adaptation module, checkpointing, and a concept diversity objective. This approach generates multiple equally accurate CBMs from a single training process, achieving greater diversity than baseline methods while consuming less memory. The resulting diverse models enable trustworthy selection, reduce inter‑class confusion, and support reliable abstention in decision‑making.
arXiv:2512. 20638v2 Announce Type: replace-cross Abstract: The evaluation of large language models relies heavily on standardized benchmarks.
arXiv:2511. 19636v2 Announce Type: replace-cross Abstract: In many machine learning problems, there may exist multiple models that achieve nearly identical predictive performance while relying on fundamentally different internal logic.
arXiv:2606. 30498v1 Announce Type: cross Abstract: Human decision-making interprets the world through high-level concepts, such as recognizing a bird by its belly color.
arXiv:2601. 21944v3 Announce Type: replace Abstract: The widespread adoption of deep learning models in computer vision has intensified concerns about interpretability.