← Techniques
Deep Learning › Techniques ›

Interpretability

36 directly classified papers

Papers per year

Papers

ICR Probe: Tracking Hidden State Dynamics for Reliable Hallucination Detection in LLMs ACL 2025
JoPA: Explaining Large Language Model’s Generation via Joint Prompt Attribution ACL 2025
Designing and Contextualising Probes for African Languages ACL 2025
Interpretable Image Classification via Non-parametric Part Prototype Learning CVPR 2025
Improving LLM Reasoning through Interpretable Role-Playing Steering EMNLP 2025
Mechanistic Understanding and Mitigation of Language Confusion in English-Centric Large Language Models EMNLP 2025
BEE: Metric-Adapted Explanations via Baseline Exploration-Exploitation AAAI 2025
Graph Segmentation and Contrastive Enhanced Explainer for Graph Neural Networks AAAI 2025
Interpreting Object-level Foundation Models via Visual Precision Search CVPR 2025
Attention IoU: Examining Biases in CelebA using Attention Maps CVPR 2025
Finer-CAM: Spotting the Difference Reveals Finer Details for Visual Explanation CVPR 2025
Position-aware Automatic Circuit Discovery ACL 2025
Revealing the Deceptiveness of Knowledge Editing: A Mechanistic Analysis of Superficial Editing ACL 2025
Improving Explainable Fact-Checking via Sentence-Level Factual Reasoning EMNLP 2024
Keep the Faith: Faithful Explanations in Convolutional Neural Networks for Case-Based Reasoning AAAI 2024
A Convolutional Neural Network Interpretable Framework for Human Ventral Visual Pathway Representation AAAI 2024
Q-SENN: Quantized Self-Explaining Neural Networks AAAI 2024
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? NIPS 2024
Interpreting Learned Feedback Patterns in Large Language Models NIPS 2024
InternalInspector I2: Robust Confidence Estimation in LLMs through Internal States EMNLP 2024
Encoding Time-Series Explanations through Self-Supervised Model Behavior Consistency NIPS 2023
VISIT: Visualizing and Interpreting the Semantic Information Flow of Transformers EMNLP 2023
Global Concept-Based Interpretability for Graph Neural Networks via Neuron Analysis AAAI 2023
Adversarial Normalization: I Can Visualize Everything (ICE) CVPR 2023
B-Cos Networks: Alignment Is All We Need for Interpretability CVPR 2022