{"total":85,"rows":[{"id":"2307.03838","title":"RADAR: Robust AI-Text Detection via Adversarial Learning","created":"2023-07-07","cited_by_count":190,"area":null},{"id":"2106.07499","title":"An Empirical Survey of Data Augmentation for Limited Data Learning in NLP","created":"2021-06-14","cited_by_count":101,"area":null},{"id":"2107.06641","title":"Trustworthy AI: A Computational Perspective","created":"2021-07-12","cited_by_count":93,"area":null},{"id":"2305.11391","title":"A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation","created":"2023-05-19","cited_by_count":93,"area":null},{"id":"2109.00544","title":"Towards Improving Adversarial Training of NLP Models","created":"2021-09-01","cited_by_count":79,"area":null},{"id":"2105.03743","title":"Certified Robustness to Text Adversarial Attacks by Randomized [MASK]","created":"2021-05-08","cited_by_count":57,"area":null},{"id":"2101.06560","title":"Adversarial Attacks On Multi-Agent Communication","created":"2021-01-17","cited_by_count":49,"area":null},{"id":"2212.10020","title":"On the Blind Spots of Model-Based Evaluation Metrics for Text Generation","created":"2022-12-20","cited_by_count":49,"area":null},{"id":"2101.08030","title":"Adversarial Attacks for Tabular Data: Application to Fraud Detection and Imbalanced Data","created":"2021-01-20","cited_by_count":45,"area":null},{"id":"2104.05942","title":"Recurrent Equilibrium Networks: Flexible Dynamic Models with Guaranteed Stability and Robustness","created":"2021-04-13","cited_by_count":45,"area":null},{"id":"2205.01287","title":"SemAttack: Natural Textual Attacks via Different Semantic Spaces","created":"2022-05-03","cited_by_count":42,"area":null},{"id":"2402.11493","title":"Benchmarking Knowledge Boundary for Large Language Models: A Different Perspective on Model Evaluation","created":"2024-02-18","cited_by_count":38,"area":null},{"id":"2206.07438","title":"Multi-Objective Hyperparameter Optimization in Machine Learning -- An Overview","created":"2022-06-15","cited_by_count":37,"area":null},{"id":"2105.11136","title":"Using Adversarial Attacks to Reveal the Statistical Bias in Machine Reading Comprehension Models","created":"2021-05-24","cited_by_count":29,"area":null},{"id":"2111.09961","title":"A Review of Adversarial Attack and Defense for Classification Methods","created":"2021-11-18","cited_by_count":29,"area":null},{"id":"2210.17004","title":"Character-level White-Box Adversarial Attacks against Transformers via Attachable Subwords Substitution","created":"2022-10-31","cited_by_count":28,"area":null},{"id":"2306.15261","title":"A Survey on Out-of-Distribution Evaluation of Neural NLP Models","created":"2023-06-27","cited_by_count":20,"area":null},{"id":"2105.14813","title":"Exploration and Exploitation: Two Ways to Improve Chinese Spelling Correction Models","created":"2021-05-31","cited_by_count":17,"area":null},{"id":"2109.02229","title":"Efficient Combinatorial Optimization for Word-level Adversarial Textual Attack","created":"2021-09-06","cited_by_count":17,"area":null},{"id":"2203.14145","title":"Reverse Engineering of Imperceptible Adversarial Image Perturbations","created":"2022-03-26","cited_by_count":17,"area":null},{"id":"2112.07605","title":"The King is Naked: on the Notion of Robustness for Natural Language Processing","created":"2021-12-13","cited_by_count":16,"area":null},{"id":"2210.03696","title":"LLMEffiChecker: Understanding and Testing Efficiency Degradation of Large Language Models","created":"2022-10-07","cited_by_count":16,"area":null},{"id":"2402.01806","title":"HQA-Attack: Toward High Quality Black-Box Hard-Label Adversarial Attack on Text","created":"2024-02-02","cited_by_count":16,"area":null},{"id":"2102.01563","title":"On Robustness of Neural Semantic Parsers","created":"2021-02-02","cited_by_count":14,"area":null},{"id":"2305.03655","title":"White-Box Multi-Objective Adversarial Attack on Dialogue Generation","created":"2023-05-05","cited_by_count":14,"area":null},{"id":"2404.08690","title":"Towards Building a Robust Toxicity Predictor","created":"2024-04-09","cited_by_count":14,"area":null},{"id":"2101.11310","title":"Adversarial Stylometry in the Wild: Transferable Lexical Substitution Attacks on Author Profiling","created":"2021-01-27","cited_by_count":13,"area":null},{"id":"2104.05996","title":"Fall of Giants: How popular text-based MLaaS fall against a simple evasion attack","created":"2021-04-13","cited_by_count":13,"area":null},{"id":"2103.00676","title":"Token-Modification Adversarial Attacks for Natural Language Processing: A Survey","created":"2021-03-01","cited_by_count":12,"area":null},{"id":"2302.00944","title":"TransFool: An Adversarial Attack against Neural Machine Translation Models","created":"2023-02-02","cited_by_count":12,"area":null},{"id":"2201.00768","title":"Robust Natural Language Processing: Recent Advances, Challenges, and Future Directions","created":"2022-01-03","cited_by_count":11,"area":null},{"id":"2202.07201","title":"Holistic Adversarial Robustness of Deep Learning Models","created":"2022-02-15","cited_by_count":11,"area":null},{"id":"2107.01943","title":"When and How to Fool Explainable Models (and Humans) with Adversarial Examples","created":"2021-07-05","cited_by_count":10,"area":null},{"id":"2303.01068","title":"Targeted Adversarial Attacks against Neural Machine Translation","created":"2023-03-02","cited_by_count":10,"area":null},{"id":"2104.05232","title":"Double Perturbation: On the Robustness of Robustness and Counterfactual Bias Evaluation","created":"2021-04-12","cited_by_count":9,"area":null},{"id":"2107.11275","title":"A Differentiable Language Model Adversarial Attack on Text Classifiers","created":"2021-07-23","cited_by_count":9,"area":null},{"id":"2401.12097","title":"An Empirical Study of In-context Learning in LLMs for Machine Translation","created":"2024-01-22","cited_by_count":9,"area":null},{"id":"2103.10133","title":"Evaluating Document Coherence Modelling","created":"2021-03-18","cited_by_count":8,"area":null},{"id":"2204.10192","title":"Residue-Based Natural Language Adversarial Attack Detection","created":"2022-04-17","cited_by_count":8,"area":null},{"id":"2304.02976","title":"Unconstrained Parametrization of Dissipative and Contracting Neural Ordinary Differential Equations","created":"2023-04-06","cited_by_count":8,"area":null},{"id":"2305.16444","title":"Don't Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text","created":"2023-05-25","cited_by_count":8,"area":null},{"id":"2111.08529","title":"Improving the robustness and accuracy of biomedical language models through adversarial training","created":"2021-11-16","cited_by_count":7,"area":null},{"id":"2405.14646","title":"Unveiling the Achilles' Heel of NLG Evaluators: A Unified Adversarial Framework Driven by Large Language Models","created":"2024-05-23","cited_by_count":6,"area":null},{"id":"2107.10443","title":"Spinning Sequence-to-Sequence Models with Meta-Backdoors","created":"2021-07-22","cited_by_count":5,"area":null},{"id":"2303.07199","title":"BeamAttack: Generating High-quality Textual Adversarial Examples through Beam Search and Mixed Semantic Spaces","created":"2023-03-09","cited_by_count":5,"area":null},{"id":"2211.00294","title":"FRSUM: Towards Faithful Abstractive Summarization via Enhancing Factual Robustness","created":"2022-11-01","cited_by_count":4,"area":null},{"id":"2308.15246","title":"A Classification-Guided Approach for Adversarial Attacks against Neural Machine Translation","created":"2023-08-29","cited_by_count":4,"area":null},{"id":"2405.04346","title":"Revisiting Character-level Adversarial Attacks for Language Models","created":"2024-05-07","cited_by_count":4,"area":null},{"id":"2210.11237","title":"Emerging Threats in Deep Learning-Based Autonomous Driving: A Comprehensive Survey","created":"2022-10-19","cited_by_count":3,"area":null},{"id":"2210.12367","title":"Precisely the Point: Adversarial Augmentations for Faithful and Informative Text Generation","created":"2022-10-22","cited_by_count":3,"area":null},{"id":"2306.05079","title":"Enhancing Robustness of AI Offensive Code Generators via Data Augmentation","created":"2023-06-08","cited_by_count":3,"area":null},{"id":"2307.12507","title":"Gradient-Based Word Substitution for Obstinate Adversarial Examples Generation in Language Models","created":"2023-07-24","cited_by_count":3,"area":null},{"id":"2101.08523","title":"Adv-OLM: Generating Textual Adversaries via OLM","created":"2021-01-21","cited_by_count":2,"area":null},{"id":"2101.08675","title":"Adversarial Machine Learning in Text Analysis and Generation","created":"2021-01-14","cited_by_count":2,"area":null},{"id":"2109.06508","title":"Tribrid: Stance Classification with Neural Inconsistency Detection","created":"2021-09-14","cited_by_count":2,"area":null},{"id":"2109.14350","title":"Call Larisa Ivanovna: Code-Switching Fools Multilingual NLU Models","created":"2021-09-29","cited_by_count":2,"area":null},{"id":"2203.15319","title":"Can NMT Understand Me? Towards Perturbation-based Evaluation of NMT Models for Code Generation","created":"2022-03-29","cited_by_count":2,"area":null},{"id":"2303.10974","title":"Translate your gibberish: black-box adversarial attack on machine translation systems","created":"2023-03-20","cited_by_count":2,"area":null},{"id":"2305.13208","title":"Iterative Adversarial Attack on Image-guided Story Ending Generation","created":"2023-05-16","cited_by_count":2,"area":null},{"id":"2307.12520","title":"Lost In Translation: Generating Adversarial Examples Robust to Round-Trip Translation","created":"2023-07-24","cited_by_count":2,"area":null},{"id":"2403.08425","title":"Specification Overfitting in Artificial Intelligence","created":"2024-03-13","cited_by_count":2,"area":null},{"id":"2405.11904","title":"A Constraint-Enforcing Reward for Adversarial Attacks on Text Classifiers","created":"2024-05-20","cited_by_count":2,"area":null},{"id":"2410.05573","title":"TaeBench: Improving Quality of Toxic Adversarial Examples","created":"2024-10-08","cited_by_count":2,"area":null},{"id":"2305.01437","title":"Sentiment Perception Adversarial Attacks on Neural Machine Translation Systems","created":"2023-05-02","cited_by_count":1,"area":null},{"id":"2306.08492","title":"A Relaxed Optimization Approach for Adversarial Attacks against Neural Machine Translation Models","created":"2023-06-14","cited_by_count":1,"area":null},{"id":"2310.01452","title":"Fooling the Textual Fooler via Randomizing Latent Representations","created":"2023-10-02","cited_by_count":1,"area":null},{"id":"2402.09874","title":"Camouflage is all you need: Evaluating and Enhancing Language Model Robustness Against Camouflage Adversarial Attacks","created":"2024-02-15","cited_by_count":1,"area":null},{"id":"2409.05021","title":"Vision-fused Attack: Advancing Aggressive and Stealthy Adversarial Text against Neural Machine Translation","created":"2024-09-08","cited_by_count":1,"area":null},{"id":"2505.16263","title":"All You Need is \"Leet\": Evading Hate-speech Detection AI","created":"2025-05-22","cited_by_count":1,"area":null},{"id":"2508.10039","title":"Multi-task Adversarial Attacks against Black-box Model with Few-shot Queries","created":"2025-08-10","cited_by_count":1,"area":null},{"id":"2101.06704","title":"Adversarial Interaction Attack: Fooling AI to Misinterpret Human Intentions","created":"2021-01-17","cited_by_count":0,"area":null},{"id":"2108.05490","title":"Attacks against Ranking Algorithms with Text Embeddings: a Case Study on Recruitment Algorithms","created":"2021-08-12","cited_by_count":0,"area":null},{"id":"2109.00417","title":"Masked Adversarial Generation for Neural Machine Translation","created":"2021-09-01","cited_by_count":0,"area":null},{"id":"2109.01558","title":"Learning Neural Models for Natural Language Processing in the Face of Distributional Shift","created":"2021-09-03","cited_by_count":0,"area":null},{"id":"2206.00192","title":"Order-sensitive Shapley Values for Evaluating Conceptual Soundness of NLP Models","created":"2022-06-01","cited_by_count":0,"area":null},{"id":"2303.05575","title":"Evaluating the Robustness of Conversational Recommender Systems by Adversarial Examples","created":"2023-03-09","cited_by_count":0,"area":null},{"id":"2403.14731","title":"Reversible Jump Attack to Textual Classifiers with Modification Reduction","created":"2024-03-21","cited_by_count":0,"area":null},{"id":"2407.05319","title":"Rethinking Targeted Adversarial Attacks For Neural Machine Translation","created":"2024-07-07","cited_by_count":0,"area":null},{"id":"2411.12473","title":"NMT-Obfuscator Attack: Ignore a sentence in translation with only one word","created":"2024-11-19","cited_by_count":0,"area":null},{"id":"2411.13867","title":"Generative Fuzzy System for Sequence Generation","created":"2024-11-21","cited_by_count":0,"area":null},{"id":"2501.12183","title":"Extend Adversarial Policy Against Neural Machine Translation via Unknown Token","created":"2025-01-21","cited_by_count":0,"area":null},{"id":"2502.07101","title":"SMAB: MAB based word Sensitivity Estimation Framework and its Applications in Adversarial Text Generation","created":"2025-02-10","cited_by_count":0,"area":null},{"id":"2507.05512","title":"Disappearing Ink: Obfuscation Breaks N-gram Code Watermarks in Theory and Practice","created":"2025-07-07","cited_by_count":0,"area":null},{"id":"2509.10790","title":"GoldenTransformer: A Modular Fault Injection Framework for Transformer Robustness Research","created":"2025-09-13","cited_by_count":0,"area":null},{"id":"2512.02567","title":"Feedback Loops and Code Perturbations in LLM-based Software Engineering: A Case Study on a C-to-Rust Translation System","created":"2025-12-02","cited_by_count":0,"area":null}],"next_cursor":null}