"model interpretability" Papers

AttriBoT: A Bag of Tricks for Efficiently Approximating Leave-One-Out Context Attribution

Fengyuan Liu, Nikhil Kandpal, Colin Raffel

Demystifying Reasoning Dynamics with Mutual Information: Thinking Tokens are Information Peaks in LLM Reasoning

Discovering Influential Neuron Path in Vision Transformers

Yifan Wang, Yifei Liu, Yingdong Shi et al.

LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching

Zhuo Cao, Xuan Zhao, Lena Krieger et al.

Looking Inward: Language Models Can Learn About Themselves by Introspection

Felix Jedidja Binder, James Chua, Tomek Korbak et al.

Manipulating Feature Visualizations with Gradient Slingshots

Dilyara Bareeva, Marina Höhne, Alexander Warnecke et al.

SHAP zero Explains Biological Sequence Models with Near-zero Marginal Cost for Future Queries

Darin Tsui, Aryan Musharaf, Yigit Efe Erginbas et al.

Topology of Reasoning: Understanding Large Reasoning Models through Reasoning Graph Properties

Gouki Minegishi, Hiroki Furuta, Takeshi Kojima et al.

Unveiling Concept Attribution in Diffusion Models

Nguyen Hung-Quang, Hoang Phan, Khoa D Doan

Accelerating the Global Aggregation of Local Explanations

Alon Mor, Yonatan Belinkov, Benny Kimelfeld

Attention Guided CAM: Visual Explanations of Vision Transformer Guided by Self-Attention

Saebom Leem, Hyunseok Seo

Attribution-based Explanations that Provide Recourse Cannot be Robust

Hidde Fokkema, Rianne de Heide, Tim van Erven

Explaining Graph Neural Networks via Structure-aware Interaction Index

Ngoc Bui, Trung Hieu Nguyen, Viet Anh Nguyen et al.

Explaining Probabilistic Models with Distributional Values

Luca Franceschi, Michele Donini, Cedric Archambeau et al.

Exploring the LLM Journey from Cognition to Expression with Linear Representations

Yuzi Yan, Jialian Li, YipinZhang et al.

Improving Neural Additive Models with Bayesian Principles

Kouroche Bouchiat, Alexander Immer, Hugo Yèche et al.

Iterative Search Attribution for Deep Neural Networks

Zhiyu Zhu, Huaming Chen, Xinyi Wang et al.

KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions

Fabian Fumagalli, Maximilian Muschalik, Patrick Kolpaczki et al.

MAPTree: Beating “Optimal” Decision Trees with Bayesian Decision Trees

Colin Sullivan, Mo Tiwari, Sebastian Thrun

MFABA: A More Faithful and Accelerated Boundary-Based Attribution Method for Deep Neural Networks

Zhiyu Zhu, Huaming Chen, Jiayu Zhang et al.

On Gradient-like Explanation under a Black-box Setting: When Black-box Explanations Become as Good as White-box

Yi Cai, Gerhard Wunder

Position: Cracking the Code of Cascading Disparity Towards Marginalized Communities

Golnoosh Farnadi, Mohammad Havaei, Negar Rostamzadeh

Position: Stop Making Unscientific AGI Performance Claims

Patrick Altmeyer, Andrew Demetriou, Antony Bartlett et al.

Provably Better Explanations with Optimized Aggregation of Feature Attributions

Thomas Decker, Ananta Bhattarai, Jindong Gu et al.

Towards Modeling Uncertainties of Self-explaining Neural Networks via Conformal Prediction

Wei Qian, Chenxu Zhao, Yangyi Li et al.