{"total":35,"rows":[{"id":"2111.02840","title":"Adversarial GLUE: A Multi-Task Benchmark for Robustness Evaluation of Language Models","created":"2021-11-04","cited_by_count":232,"area":null},{"id":"2212.10264","title":"ReCode: Robustness Evaluation of Code Generation Models","created":"2022-12-20","cited_by_count":103,"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":"2101.06969","title":"Red Alarm for Pre-trained Models: Universal Vulnerability to Neuron-Level Backdoor Attacks","created":"2021-01-18","cited_by_count":49,"area":null},{"id":"2112.11668","title":"How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness?","created":"2021-12-22","cited_by_count":49,"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":"2303.11470","title":"Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor Watermarking","created":"2023-03-20","cited_by_count":37,"area":null},{"id":"2110.15317","title":"Bridge the Gap Between CV and NLP! A Gradient-based Textual Adversarial Attack Framework","created":"2021-10-28","cited_by_count":28,"area":null},{"id":"2103.09593","title":"Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots","created":"2021-03-17","cited_by_count":23,"area":null},{"id":"2109.04367","title":"Multi-granularity Textual Adversarial Attack with Behavior Cloning","created":"2021-09-09","cited_by_count":21,"area":null},{"id":"2106.01452","title":"BERT-Defense: A Probabilistic Model Based on BERT to Combat Cognitively Inspired Orthographic Adversarial Attacks","created":"2021-06-02","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":"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":"2107.11252","title":"Adversarial Reinforced Instruction Attacker for Robust Vision-Language Navigation","created":"2021-07-23","cited_by_count":15,"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":"2202.13145","title":"QuoteR: A Benchmark of Quote Recommendation for Writing","created":"2022-02-26","cited_by_count":10,"area":null},{"id":"2203.05948","title":"Block-Sparse Adversarial Attack to Fool Transformer-Based Text Classifiers","created":"2022-03-11","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":"2109.05698","title":"Detecting Textual Adversarial Examples through Randomized Substitution and Vote","created":"2021-09-13","cited_by_count":8,"area":null},{"id":"2202.13352","title":"HiCLRE: A Hierarchical Contrastive Learning Framework for Distantly Supervised Relation Extraction","created":"2022-02-27","cited_by_count":8,"area":null},{"id":"2201.04286","title":"Evolutionary Action Selection for Gradient-based Policy Learning","created":"2022-01-12","cited_by_count":7,"area":null},{"id":"2208.08010","title":"ShortcutLens: A Visual Analytics Approach for Exploring Shortcuts in Natural Language Understanding Dataset","created":"2022-08-17","cited_by_count":6,"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":"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":"2101.08523","title":"Adv-OLM: Generating Textual Adversaries via OLM","created":"2021-01-21","cited_by_count":2,"area":null},{"id":"2210.12233","title":"TCAB: A Large-Scale Text Classification Attack Benchmark","created":"2022-10-21","cited_by_count":2,"area":null},{"id":"2105.12585","title":"Automatic Construction of Sememe Knowledge Bases via Dictionaries","created":"2021-05-26","cited_by_count":1,"area":null},{"id":"2502.19756","title":"PolyPrompt: Automating Knowledge Extraction from Multilingual Language Models with Dynamic Prompt Generation","created":"2025-02-27","cited_by_count":1,"area":null},{"id":"2110.05748","title":"SEPP: Similarity Estimation of Predicted Probabilities for Defending and Detecting Adversarial Text","created":"2021-10-12","cited_by_count":0,"area":null},{"id":"2207.02639","title":"Adversarial Robustness of Visual Dialog","created":"2022-07-06","cited_by_count":0,"area":null},{"id":"2207.08083","title":"Towards Explainability in NLP: Analyzing and Calculating Word Saliency through Word Properties","created":"2022-07-17","cited_by_count":0,"area":null},{"id":"2409.17474","title":"Reducing and Exploiting Data Augmentation Noise through Meta Reweighting Contrastive Learning for Text Classification","created":"2024-09-26","cited_by_count":0,"area":null},{"id":"2412.18706","title":"SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation","created":"2024-12-24","cited_by_count":0,"area":null}],"next_cursor":null}