arXiv AI
Oct 12

Hide and Seek: HIDE Concepts to SEEK explanations for black box models

Hide and Seek (HnS) is a concept‑level attribution method that interprets black‑box vision models without needing model weights or labeled concept datasets. It retrieves concept‑aligned images via a vision‑language model, localizes and masks the concept region, and measures attribution by observing changes in the model’s output. HnS was validated on CelebA and COCO‑Stuff, showing stronger agreement with ground‑truth attributions than existing methods, and was applied to tasks like image captioning and satellite image segmentation where other concept attribution methods fail.

By Bhanu Tokas, Rahul Nair, Rockwell Jackson, Hannah Kerner
arXiv Machine Learning
Oct 8

For Those Who Believe in Faithfulness: Optimizing the Area Under Insertion and Deletion Curves for Ranking Relative Feature Importance

The paper introduces Ra‑NEM, a method that optimizes the area under insertion and deletion curves to improve the faithfulness of feature importance attributions in machine learning models. By deriving an objective function from faithfulness and approximating its gradient, the authors link insertion curves to top‑k feature selection, enabling a loss function that measures attribution quality. Ra‑NEM, integrated with the neural explanation mask framework, works with any differentiable model, yielding accurate, robust, and fast attributions that outperform existing algorithms on multiple XAI metrics.

By Bj{\o}rn Leth M{\o}ller, Bulat Ibragimov, Christian Igel
arXiv Computer Vision
Aug 25

Where Not to Learn: Prior-Aligned Training with Subset-based Attribution Constraints

arXiv:2602.07008v5 Announce Type: replace Abstract: Reliable models should not only predict correctly, but also base their decisions on acceptable evidence. However, conventional supervised learning...

By Ruoyu Chen, Shangquan Sun, Xiaoqing Guo, Kangwei Liu, Sanyi Zhang, Zhangcheng Wang, Shiming Liu, Qunli Zhang, Wei Wang, Hua Zhang, Xiaochun Cao
arXiv Machine Learning
Sep 23

Matryoshka attribution: Learning to attribute language model outputs to representations and weights

Matryoshka Attribution (MAttr) is a mask‑learning method that identifies nested subsets of a language model’s internal components by minimizing downstream loss. It uses a differentiable sigmoid top‑k operator and randomizes sparsity during training to produce an attribution ordering of components. MAttr tops the Mechanistic Interpretability Benchmark leaderboard and can be applied via reinforcement learning to pinpoint weight changes that control behaviors such as refusal in Llama 3.1 8B Instruct, where restoring just 1% of weights removes refusals while preserving capabilities.

By Aryaman Arora, Kirill Acharya, Nathan Hu, Yanzhe Zhang, Noah Goodman, Dan Jurafsky, Christopher Potts