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Robustness

133 papers

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Papers

Flip-Flop Consistency: Unsupervised Training for Robustness to Prompt Perturbations in LLMs ACL 2026
A Data-Centric Approach to Generalizable Speech Deepfake Detection ACL 2026
DeepGuard: Secure Code Generation via Multi-Layer Semantic Aggregation ACL 2026
Toward Robust In-Context Learning: Leveraging Out-of-distribution Proxies for Target Inaccessible Demonstration Retrieval ACL 2026
Stop Hardening Everything: A Training-Free Neuron-Level Defense for Neural Ranking Models ACL 2026
Exons-Detect: Identifying and Amplifying Exonic Tokens via Hidden-State Discrepancy for Robust AI-Generated Text Detection ACL 2026
LLMs in Sarcasm Detection? It’s elementary! (Or is it?) ACL 2026
QA-MoE: Towards a Continuous Reliability Spectrum with Quality-Aware Mixture of Experts for Robust Multimodal Sentiment Analysis ACL 2026
DARM: Distribution-Aware Reward Modeling by Alleviating Biases from Low Preference-Context Dependency Data ACL 2026
When Benchmarks Leak: Inference-Time Decontamination for LLMs ACL 2026
Debiasing Logical Fallacy Detection for Real-World Robustness via Counterfactually Augmented Data ACL 2026
Measuring and Mitigating Shortcut Reliance in Language Models with Probe-Based Representation Entanglement ACL 2026
Improving Generalization of Universal Adversarial Perturbation via Dynamic Maximin Optimization AAAI 2025
CAPTURE: Context-Aware Prompt Injection Testing and Robustness Enhancement ACL 2025
Words or Vision: Do Vision-Language Models Have Blind Faith in Text? CVPR 2025
SATA: Spatial Autocorrelation Token Analysis for Enhancing the Robustness of Vision Transformers CVPR 2025
CADRef: Robust Out-of-Distribution Detection via Class-Aware Decoupled Relative Feature Leveraging CVPR 2025
Benchmarking and Mitigating MCQA Selection Bias of Large Vision-Language Models EMNLP 2025
Improving robustness to corruptions with multiplicative weight perturbations NIPS 2024
Learning predictable and robust neural representations by straightening image sequences NIPS 2024
Diffusion Models are Certifiably Robust Classifiers NIPS 2024
Noisy Ostracods: A Fine-Grained, Imbalanced Real-World Dataset for Benchmarking Robust Machine Learning and Label Correction Methods NIPS 2024
Dual Risk Minimization: Towards Next-Level Robustness in Fine-tuning Zero-Shot Models NIPS 2024
Improving Robustness of 3D Point Cloud Recognition from a Fourier Perspective NIPS 2024
TabularBench: Benchmarking Adversarial Robustness for Tabular Deep Learning in Real-world Use-cases NIPS 2024