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Artificial Intelligence
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Core AI
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Interpretability
7,318 papers
Papers per year
2003: 1
2006: 1
2007: 1
2008: 1
2009: 1
2010: 5
2012: 2
2013: 10
2014: 7
2015: 14
2016: 27
2017: 84
2018: 196
2019: 395
2020: 488
2021: 771
2022: 823
2023: 954
2024: 1360
2025: 1713
2026: 464
Papers
Localize, Understand, Collaborate: Semantic-Aware Dragging via Intention Reasoner
NIPS 2024
LeDex: Training LLMs to Better Self-Debug and Explain Code
NIPS 2024
Interpreting Learned Feedback Patterns in Large Language Models
NIPS 2024
Almost-Linear RNNs Yield Highly Interpretable Symbolic Codes in Dynamical Systems Reconstruction
NIPS 2024
Learning Discrete Concepts in Latent Hierarchical Models
NIPS 2024
Diff-eRank: A Novel Rank-Based Metric for Evaluating Large Language Models
NIPS 2024
LG-CAV: Train Any Concept Activation Vector with Language Guidance
NIPS 2024
Interpreting and Analysing CLIP's Zero-Shot Image Classification via Mutual Knowledge
NIPS 2024
GAIA: Rethinking Action Quality Assessment for AI-Generated Videos
NIPS 2024
Interpretable Image Classification with Adaptive Prototype-based Vision Transformers
NIPS 2024
LACIE: Listener-Aware Finetuning for Calibration in Large Language Models
NIPS 2024
Beyond Accuracy: Ensuring Correct Predictions With Correct Rationales
NIPS 2024
Multi-Object Hallucination in Vision Language Models
NIPS 2024
Data-faithful Feature Attribution: Mitigating Unobservable Confounders via Instrumental Variables
NIPS 2024
Biologically Inspired Learning Model for Instructed Vision
NIPS 2024
Flaws can be Applause: Unleashing Potential of Segmenting Ambiguous Objects in SAM
NIPS 2024
Questioning the Survey Responses of Large Language Models
NIPS 2024
Learning to Understand: Identifying Interactions via the Möbius Transform
NIPS 2024
MARVEL: Multidimensional Abstraction and Reasoning through Visual Evaluation and Learning
NIPS 2024
Task Confusion and Catastrophic Forgetting in Class-Incremental Learning: A Mathematical Framework for Discriminative and Generative Modelings
NIPS 2024
Measuring Per-Unit Interpretability at Scale Without Humans
NIPS 2024
Decoding-Time Language Model Alignment with Multiple Objectives
NIPS 2024
SETLEXSEM CHALLENGE: Using Set Operations to Evaluate the Lexical and Semantic Robustness of Language Models
NIPS 2024
Towards the Dynamics of a DNN Learning Symbolic Interactions
NIPS 2024
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized Tasks
NIPS 2024
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