{"total":132,"rows":[{"id":"2302.13007","title":"AugGPT: Leveraging ChatGPT for Text Data Augmentation","created":"2023-02-25","cited_by_count":130,"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":"2106.01065","title":"Towards Robustness of Text-to-SQL Models against Synonym Substitution","created":"2021-06-02","cited_by_count":110,"area":null},{"id":"2103.11943","title":"BERT: A Review of Applications in Natural Language Processing and Understanding","created":"2021-03-22","cited_by_count":109,"area":null},{"id":"2109.00544","title":"Towards Improving Adversarial Training of NLP Models","created":"2021-09-01","cited_by_count":79,"area":null},{"id":"2110.07736","title":"Identifying and Mitigating Spurious Correlations for Improving Robustness in NLP Models","created":"2021-10-14","cited_by_count":76,"area":null},{"id":"2108.12777","title":"Searching for an Effective Defender: Benchmarking Defense against Adversarial Word Substitution","created":"2021-08-29","cited_by_count":56,"area":null},{"id":"2505.12540","title":"Harnessing the Universal Geometry of Embeddings","created":"2025-05-18","cited_by_count":55,"area":null},{"id":"2207.01193","title":"A Customized Text Sanitization Mechanism with Differential Privacy","created":"2022-07-04","cited_by_count":52,"area":null},{"id":"2210.09421","title":"Deepfake Text Detection: Limitations and Opportunities","created":"2022-10-17","cited_by_count":37,"area":null},{"id":"2203.01677","title":"Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation","created":"2022-03-03","cited_by_count":32,"area":null},{"id":"2105.14553","title":"Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance Sacrifice","created":"2021-05-30","cited_by_count":30,"area":null},{"id":"2108.04990","title":"Perturbing Inputs for Fragile Interpretations in Deep Natural Language Processing","created":"2021-08-11","cited_by_count":30,"area":null},{"id":"2303.16756","title":"Large Language Models for Healthcare Data Augmentation: An Example on Patient-Trial Matching","created":"2023-03-24","cited_by_count":30,"area":null},{"id":"2308.09861","title":"Black-box Adversarial Attacks against Dense Retrieval Models: A Multi-view Contrastive Learning Method","created":"2023-08-19","cited_by_count":29,"area":null},{"id":"2103.11576","title":"Grey-box Adversarial Attack And Defence For Sentiment Classification","created":"2021-03-22","cited_by_count":28,"area":null},{"id":"2212.09994","title":"Towards Robustness of Text-to-SQL Models Against Natural and Realistic Adversarial Table Perturbation","created":"2022-12-20","cited_by_count":28,"area":null},{"id":"2404.01907","title":"Humanizing Machine-Generated Content: Evading AI-Text Detection through Adversarial Attack","created":"2024-04-02","cited_by_count":27,"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":"2205.10710","title":"Phrase-level Textual Adversarial Attack with Label Preservation","created":"2022-05-22","cited_by_count":23,"area":null},{"id":"2206.08575","title":"Query-Efficient and Scalable Black-Box Adversarial Attacks on Discrete Sequential Data via Bayesian Optimization","created":"2022-06-17","cited_by_count":22,"area":null},{"id":"2104.08645","title":"Improving Zero-Shot Cross-Lingual Transfer Learning via Robust Training","created":"2021-04-17","cited_by_count":21,"area":null},{"id":"2109.00430","title":"ReMeDi: Resources for Multi-domain, Multi-service, Medical Dialogues","created":"2021-09-01","cited_by_count":20,"area":null},{"id":"2211.02878","title":"Textual Manifold-based Defense Against Natural Language Adversarial Examples","created":"2022-11-05","cited_by_count":19,"area":null},{"id":"2407.08521","title":"Emergent Visual-Semantic Hierarchies in Image-Text Representations","created":"2024-07-11","cited_by_count":19,"area":null},{"id":"2109.04775","title":"A Strong Baseline for Query Efficient Attacks in a Black Box Setting","created":"2021-09-10","cited_by_count":16,"area":null},{"id":"2110.11589","title":"Text Counterfactuals via Latent Optimization and Shapley-Guided Search","created":"2021-10-22","cited_by_count":16,"area":null},{"id":"2203.14207","title":"Text Adversarial Purification as Defense against Adversarial Attacks","created":"2022-03-27","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":"2210.15221","title":"TASA: Deceiving Question Answering Models by Twin Answer Sentences Attack","created":"2022-10-27","cited_by_count":15,"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":"2206.03178","title":"Fooling Explanations in Text Classifiers","created":"2022-06-07","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":"2202.13817","title":"Robust Textual Embedding against Word-level Adversarial Attacks","created":"2022-02-28","cited_by_count":12,"area":null},{"id":"2203.12709","title":"Adversarial Training for Improving Model Robustness? Look at Both Prediction and Interpretation","created":"2022-03-23","cited_by_count":12,"area":null},{"id":"2209.03755","title":"Fact-Saboteurs: A Taxonomy of Evidence Manipulation Attacks against Fact-Verification Systems","created":"2022-09-07","cited_by_count":12,"area":null},{"id":"2210.12170","title":"Discovering Differences in the Representation of People using Contextualized Semantic Axes","created":"2022-10-21","cited_by_count":12,"area":null},{"id":"2305.06522","title":"Randomized Smoothing with Masked Inference for Adversarially Robust Text Classifications","created":"2023-05-11","cited_by_count":11,"area":null},{"id":"2103.15022","title":"'Just because you are right, doesn't mean I am wrong': Overcoming a Bottleneck in the Development and Evaluation of Open-Ended Visual Question Answering (VQA) Tasks","created":"2021-03-28","cited_by_count":10,"area":null},{"id":"2201.08193","title":"TextHacker: Learning based Hybrid Local Search Algorithm for Text Hard-label Adversarial Attack","created":"2022-01-20","cited_by_count":10,"area":null},{"id":"2205.03815","title":"Beyond Distributional Hypothesis: Let Language Models Learn Meaning-Text Correspondence","created":"2022-05-08","cited_by_count":10,"area":null},{"id":"2305.08010","title":"ProKnow: Process Knowledge for Safety Constrained and Explainable Question Generation for Mental Health Diagnostic Assistance","created":"2023-05-13","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":"2109.07403","title":"BERT is Robust! A Case Against Synonym-Based Adversarial Examples in Text Classification","created":"2021-09-15","cited_by_count":9,"area":null},{"id":"2211.05523","title":"Impact of Adversarial Training on Robustness and Generalizability of Language Models","created":"2022-11-10","cited_by_count":9,"area":null},{"id":"2302.02568","title":"Less is More: Understanding Word-level Textual Adversarial Attack via n-gram Frequency Descend","created":"2023-02-06","cited_by_count":9,"area":null},{"id":"2308.00319","title":"LimeAttack: Local Explainable Method for Textual Hard-Label Adversarial Attack","created":"2023-08-01","cited_by_count":9,"area":null},{"id":"2109.05698","title":"Detecting Textual Adversarial Examples through Randomized Substitution and Vote","created":"2021-09-13","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":"2306.12756","title":"On the Robustness of Generative Retrieval Models: An Out-of-Distribution Perspective","created":"2023-06-22","cited_by_count":8,"area":null},{"id":"2205.12502","title":"The Dialog Must Go On: Improving Visual Dialog via Generative Self-Training","created":"2022-05-25","cited_by_count":7,"area":null},{"id":"2104.08453","title":"R&R: Metric-guided Adversarial Sentence Generation","created":"2021-04-17","cited_by_count":6,"area":null},{"id":"2305.17440","title":"Modeling Adversarial Attack on Pre-trained Language Models as Sequential Decision Making","created":"2023-05-27","cited_by_count":6,"area":null},{"id":"2306.02623","title":"Do-GOOD: Towards Distribution Shift Evaluation for Pre-Trained Visual Document Understanding Models","created":"2023-06-05","cited_by_count":6,"area":null},{"id":"2306.15268","title":"Can Pretrained Language Models Derive Correct Semantics from Corrupt Subwords under Noise?","created":"2023-06-27","cited_by_count":6,"area":null},{"id":"2108.10015","title":"Semantic-Preserving Adversarial Text Attacks","created":"2021-08-23","cited_by_count":5,"area":null},{"id":"2109.07926","title":"Don't Search for a Search Method -- Simple Heuristics Suffice for Adversarial Text Attacks","created":"2021-09-16","cited_by_count":5,"area":null},{"id":"2208.10251","title":"Rethinking Textual Adversarial Defense for Pre-trained Language Models","created":"2022-07-21","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":"2310.08889","title":"PerturbScore: Connecting Discrete and Continuous Perturbations in NLP","created":"2023-10-13","cited_by_count":5,"area":null},{"id":"2311.00508","title":"Robustness Tests for Automatic Machine Translation Metrics with Adversarial Attacks","created":"2023-11-01","cited_by_count":5,"area":null},{"id":"2311.05018","title":"Towards Effective Paraphrasing for Information Disguise","created":"2023-11-08","cited_by_count":5,"area":null},{"id":"2502.04643","title":"Confidence Elicitation: A New Attack Vector for Large Language Models","created":"2025-02-07","cited_by_count":5,"area":null},{"id":"2107.07610","title":"Self-Supervised Contrastive Learning with Adversarial Perturbations for Defending Word Substitution-based Attacks","created":"2021-07-15","cited_by_count":4,"area":null},{"id":"2210.02844","title":"Are Synonym Substitution Attacks Really Synonym Substitution Attacks?","created":"2022-10-06","cited_by_count":4,"area":null},{"id":"2303.01234","title":"Frauds Bargain Attack: Generating Adversarial Text Samples via Word Manipulation Process","created":"2023-03-01","cited_by_count":4,"area":null},{"id":"2310.19152","title":"BERT Lost Patience Won't Be Robust to Adversarial Slowdown","created":"2023-10-29","cited_by_count":4,"area":null},{"id":"2409.02649","title":"OpenFact at CheckThat! 2024: Combining Multiple Attack Methods for Effective Adversarial Text Generation","created":"2024-09-04","cited_by_count":4,"area":null},{"id":"2107.10474","title":"Back-Translated Task Adaptive Pretraining: Improving Accuracy and Robustness on Text Classification","created":"2021-07-22","cited_by_count":3,"area":null},{"id":"2109.11308","title":"Breaking BERT: Understanding its Vulnerabilities for Named Entity Recognition through Adversarial Attack","created":"2021-09-23","cited_by_count":3,"area":null},{"id":"2203.11199","title":"Distinguishing Non-natural from Natural Adversarial Samples for More Robust Pre-trained Language Model","created":"2022-03-19","cited_by_count":3,"area":null},{"id":"2206.12284","title":"Robustness of Explanation Methods for NLP Models","created":"2022-06-24","cited_by_count":3,"area":null},{"id":"2212.02108","title":"Human-in-the-Loop Hate Speech Classification in a Multilingual Context","created":"2022-12-05","cited_by_count":3,"area":null},{"id":"2303.11143","title":"Adversarial Attacks against Binary Similarity Systems","created":"2023-03-20","cited_by_count":3,"area":null},{"id":"2306.02488","title":"Adversary for Social Good: Leveraging Adversarial Attacks to Protect Personal Attribute Privacy","created":"2023-06-04","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":"2403.18423","title":"SemRoDe: Macro Adversarial Training to Learn Representations That are Robust to Word-Level Attacks","created":"2024-03-27","cited_by_count":3,"area":null},{"id":"2110.01804","title":"A Survey On Neural Word Embeddings","created":"2021-10-05","cited_by_count":2,"area":null},{"id":"2203.12298","title":"Input-specific Attention Subnetworks for Adversarial Detection","created":"2022-03-23","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":"2204.13853","title":"Detecting Textual Adversarial Examples Based on Distributional Characteristics of Data Representations","created":"2022-04-29","cited_by_count":2,"area":null},{"id":"2209.15197","title":"Evaluation of taxonomic and neural embedding methods for calculating semantic similarity","created":"2022-09-30","cited_by_count":2,"area":null},{"id":"2210.11598","title":"Identifying Human Strategies for Generating Word-Level Adversarial Examples","created":"2022-10-20","cited_by_count":2,"area":null},{"id":"2210.17406","title":"Emergent Linguistic Structures in Neural Networks are Fragile","created":"2022-10-31","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":"2310.18930","title":"Retrofitting Light-weight Language Models for Emotions using Supervised Contrastive Learning","created":"2023-10-29","cited_by_count":2,"area":null},{"id":"2310.19177","title":"Robustifying Language Models with Test-Time Adaptation","created":"2023-10-29","cited_by_count":2,"area":null},{"id":"2402.09344","title":"Generating Diverse Translation with Perturbed kNN-MT","created":"2024-02-14","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":"2405.17130","title":"Explaining the role of Intrinsic Dimensionality in Adversarial Training","created":"2024-05-27","cited_by_count":2,"area":null},{"id":"2408.13985","title":"TF-Attack: Transferable and Fast Adversarial Attacks on Large Language Models","created":"2024-08-26","cited_by_count":2,"area":null},{"id":"2507.03473","title":"Beyond Weaponization: NLP Security for Medium and Lower-Resourced Languages in Their Own Right","created":"2025-07-04","cited_by_count":2,"area":null},{"id":"2103.08306","title":"ReinforceBug: A Framework to Generate Adversarial Textual Examples","created":"2021-03-11","cited_by_count":1,"area":null},{"id":"2106.07047","title":"Target Model Agnostic Adversarial Attacks with Query Budgets on Language Understanding Models","created":"2021-06-13","cited_by_count":1,"area":null},{"id":"2112.06276","title":"Quantifying and Understanding Adversarial Examples in Discrete Input Spaces","created":"2021-12-12","cited_by_count":1,"area":null},{"id":"2206.09458","title":"A Universal Adversarial Policy for Text Classifiers","created":"2022-06-19","cited_by_count":1,"area":null},{"id":"2208.08029","title":"A Context-Aware Approach for Textual Adversarial Attack through Probability Difference Guided Beam Search","created":"2022-08-17","cited_by_count":1,"area":null},{"id":"2209.15202","title":"Synonym Detection Using Syntactic Dependency And Neural Embeddings","created":"2022-09-30","cited_by_count":1,"area":null},{"id":"2210.00754","title":"Lexical semantics enhanced neural word embeddings","created":"2022-10-03","cited_by_count":1,"area":null},{"id":"2307.02094","title":"DARE: Towards Robust Text Explanations in Biomedical and Healthcare Applications","created":"2023-07-05","cited_by_count":1,"area":null}],"next_cursor":"100"}