{"total":42,"rows":[{"id":"2312.02003","title":"A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Ugly","created":"2023-12-04","cited_by_count":387,"area":null},{"id":"2107.03158","title":"A Survey on Data Augmentation for Text Classification","created":"2021-07-07","cited_by_count":111,"area":null},{"id":"2108.07344","title":"IsoScore: Measuring the Uniformity of Embedding Space Utilization","created":"2021-08-16","cited_by_count":53,"area":null},{"id":"2207.01164","title":"Aug-NeRF: Training Stronger Neural Radiance Fields with Triple-Level Physically-Grounded Augmentations","created":"2022-07-04","cited_by_count":46,"area":null},{"id":"2312.08880","title":"GenDet: Towards Good Generalizations for AI-Generated Image Detection","created":"2023-12-12","cited_by_count":36,"area":null},{"id":"2109.04588","title":"BERT, mBERT, or BiBERT? A Study on Contextualized Embeddings for Neural Machine Translation","created":"2021-09-09","cited_by_count":35,"area":null},{"id":"2312.02614","title":"Prompt Optimization via Adversarial In-Context Learning","created":"2023-12-05","cited_by_count":33,"area":null},{"id":"2109.03127","title":"Rare Tokens Degenerate All Tokens: Improving Neural Text Generation via Adaptive Gradient Gating for Rare Token Embeddings","created":"2021-09-07","cited_by_count":27,"area":null},{"id":"2103.00397","title":"Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective","created":"2021-02-28","cited_by_count":25,"area":null},{"id":"2203.06414","title":"A Survey of Adversarial Defences and Robustness in NLP","created":"2022-03-12","cited_by_count":23,"area":null},{"id":"2104.01853","title":"Rethinking Perturbations in Encoder-Decoders for Fast Training","created":"2021-04-05","cited_by_count":17,"area":null},{"id":"2107.11252","title":"Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation","created":"2021-07-23","cited_by_count":15,"area":null},{"id":"2110.14855","title":"CAP: Co-Adversarial Perturbation on Weights and Features for Improving Generalization of Graph Neural Networks","created":"2021-10-28","cited_by_count":15,"area":null},{"id":"2208.10010","title":"NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs","created":"2022-08-22","cited_by_count":14,"area":null},{"id":"2301.04075","title":"Benchmarking Robustness in Neural Radiance Fields","created":"2023-01-10","cited_by_count":13,"area":null},{"id":"2305.00237","title":"A Review of ChatGPT Applications in Education, Marketing, Software Engineering, and Healthcare: Benefits, Drawbacks, and Research Directions","created":"2023-04-29","cited_by_count":12,"area":null},{"id":"2103.12171","title":"Adversarial Feature Augmentation and Normalization for Visual Recognition","created":"2021-03-22","cited_by_count":11,"area":null},{"id":"2104.04946","title":"UniDrop: A Simple yet Effective Technique to Improve Transformer without Extra Cost","created":"2021-04-11","cited_by_count":11,"area":null},{"id":"2305.04557","title":"Toward Adversarial Training on Contextualized Language Representation","created":"2023-05-08","cited_by_count":10,"area":null},{"id":"2104.07284","title":"Consistency Training with Virtual Adversarial Discrete Perturbation","created":"2021-04-15","cited_by_count":9,"area":null},{"id":"2206.02368","title":"Bi-SimCut: A Simple Strategy for Boosting Neural Machine Translation","created":"2022-06-06","cited_by_count":8,"area":null},{"id":"2206.12608","title":"Adversarial Self-Attention for Language Understanding","created":"2022-06-25","cited_by_count":8,"area":null},{"id":"2104.04886","title":"Adversarial Regularization as Stackelberg Game: An Unrolled Optimization Approach","created":"2021-04-11","cited_by_count":7,"area":null},{"id":"2501.19040","title":"Towards the Worst-case Robustness of Large Language Models","created":"2025-01-31","cited_by_count":7,"area":null},{"id":"2410.15042","title":"Adversarial Training: A Survey","created":"2024-10-19","cited_by_count":6,"area":null},{"id":"2103.03457","title":"IOT: Instance-wise Layer Reordering for Transformer Structures","created":"2021-03-05","cited_by_count":5,"area":null},{"id":"2101.01568","title":"Adversarially trained LSTMs on reduced order models of urban air pollution simulations","created":"2021-01-05","cited_by_count":4,"area":null},{"id":"2104.06297","title":"Adversarial autoencoders and adversarial LSTM for improved forecasts of urban air pollution simulations","created":"2021-04-13","cited_by_count":4,"area":null},{"id":"2311.17228","title":"Survey on AI Ethics: A Socio-technical Perspective","created":"2023-11-28","cited_by_count":4,"area":null},{"id":"2502.08441","title":"Better Embeddings with Coupled Adam","created":"2025-02-12","cited_by_count":4,"area":null},{"id":"2603.26663","title":"Weight Tying Biases Token Embeddings Towards the Output Space","created":"2026-03-27","cited_by_count":3,"area":null},{"id":"2107.04677","title":"Noisy Training Improves E2E ASR for the Edge","created":"2021-07-09","cited_by_count":2,"area":null},{"id":"2206.02761","title":"Dual Decomposition of Convex Optimization Layers for Consistent Attention in Medical Images","created":"2022-06-06","cited_by_count":2,"area":null},{"id":"2507.21750","title":"Adversarial Defence without Adversarial Defence: Enhancing Language Model Robustness via Instance-level Principal Component Removal","created":"2025-07-29","cited_by_count":2,"area":null},{"id":"2109.07048","title":"ARCH: Efficient Adversarial Regularized Training with Caching","created":"2021-09-15","cited_by_count":1,"area":null},{"id":"2601.16070","title":"On damage of interpolation to adversarial robustness in regression","created":"2026-01-22","cited_by_count":1,"area":null},{"id":"2103.03108","title":"An empirical analysis of phrase-based and neural machine translation","created":"2021-03-04","cited_by_count":0,"area":null},{"id":"2112.10543","title":"Spiral Language Modeling","created":"2021-12-20","cited_by_count":0,"area":null},{"id":"2112.11776","title":"The Importance of the Current Input in Sequence Modeling","created":"2021-12-22","cited_by_count":0,"area":null},{"id":"2201.12469","title":"ScaLA: Accelerating Adaptation of Pre-Trained Transformer-Based Language Models via Efficient Large-Batch Adversarial Noise","created":"2022-01-29","cited_by_count":0,"area":null},{"id":"2406.09967","title":"Bag of Lies: Robustness in Continuous Pre-training BERT","created":"2024-06-14","cited_by_count":0,"area":null},{"id":"2603.13356","title":"Learning When to Trust in Contextual Social Bandits","created":"2026-03-09","cited_by_count":0,"area":null}],"next_cursor":null}