{"total":92,"rows":[{"id":"2201.11990","title":"Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model","created":"2022-01-28","cited_by_count":559,"area":null},{"id":"2205.05131","title":"UL2: Unifying Language Learning Paradigms","created":"2022-05-10","cited_by_count":295,"area":null},{"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":"2210.12353","title":"Leveraging Large Language Models for Multiple Choice Question Answering","created":"2022-10-22","cited_by_count":203,"area":null},{"id":"2101.11038","title":"Muppet: Massive Multi-task Representations with Pre-Finetuning","created":"2021-01-26","cited_by_count":192,"area":null},{"id":"2112.08313","title":"Measure and Improve Robustness in NLP Models: A Survey","created":"2021-12-15","cited_by_count":107,"area":null},{"id":"2103.15670","title":"On the Adversarial Robustness of Vision Transformers","created":"2021-03-29","cited_by_count":106,"area":null},{"id":"2306.04618","title":"Revisiting Out-of-distribution Robustness in NLP: Benchmark, Analysis, and LLMs Evaluations","created":"2023-06-07","cited_by_count":97,"area":null},{"id":"2105.00164","title":"Hidden Backdoors in Human-Centric Language Models","created":"2021-05-01","cited_by_count":90,"area":"safety"},{"id":"2405.20978","title":"Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training","created":"2024-05-31","cited_by_count":89,"area":"lr"},{"id":"2411.09523","title":"Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents","created":"2024-11-14","cited_by_count":65,"area":"agents"},{"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":"2306.04933","title":"InfoPrompt: Information-Theoretic Soft Prompt Tuning for Natural Language Understanding","created":"2023-06-08","cited_by_count":30,"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":"2212.09254","title":"TextGrad: Advancing Robustness Evaluation in NLP by Gradient-Driven Optimization","created":"2022-12-19","cited_by_count":21,"area":null},{"id":"2509.21834","title":"RobustFlow: Towards Robust Agentic Workflow Generation","created":"2025-09-26","cited_by_count":20,"area":"agents"},{"id":"2405.00622","title":"Causal Evaluation of Language Models","created":"2024-05-01","cited_by_count":18,"area":"lr"},{"id":"2206.05511","title":"Improving the Adversarial Robustness of NLP Models by Information Bottleneck","created":"2022-06-11","cited_by_count":17,"area":null},{"id":"2211.03013","title":"Robust Lottery Tickets for Pre-trained Language Models","created":"2022-11-06","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":"2203.10378","title":"On Robust Prefix-Tuning for Text Classification","created":"2022-03-19","cited_by_count":15,"area":null},{"id":"2308.13577","title":"Text Style Transfer Evaluation Using Large Language Models","created":"2023-08-25","cited_by_count":13,"area":null},{"id":"2110.08419","title":"Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding","created":"2021-10-16","cited_by_count":12,"area":null},{"id":"2210.09404","title":"Measures of Information Reflect Memorization Patterns","created":"2022-10-17","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":"2305.06522","title":"Randomized Smoothing with Masked Inference for Adversarially Robust Text Classifications","created":"2023-05-11","cited_by_count":11,"area":null},{"id":"2402.15989","title":"PIDformer: Transformer Meets Control Theory","created":"2024-02-25","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":"2410.10924","title":"A Benchmark Suite for Evaluating Neural Mutual Information Estimators on Unstructured Datasets","created":"2024-10-14","cited_by_count":10,"area":null},{"id":"2211.16220","title":"Which Shortcut Solution Do Question Answering Models Prefer to Learn?","created":"2022-11-29","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":"2303.07320","title":"Model-tuning Via Prompts Makes NLP Models Adversarially Robust","created":"2023-03-13","cited_by_count":9,"area":null},{"id":"2112.09237","title":"PECO: Examining Single Sentence Label Leakage in Natural Language Inference Datasets through Progressive Evaluation of Cluster Outliers","created":"2021-12-16","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":"2302.02023","title":"TextShield: Beyond Successfully Detecting Adversarial Sentences in Text Classification","created":"2023-02-03","cited_by_count":8,"area":null},{"id":"2302.04618","title":"Robust Question Answering against Distribution Shifts with Test-Time Adaptation: An Empirical Study","created":"2023-02-09","cited_by_count":8,"area":null},{"id":"2302.10701","title":"Scalable Infomin Learning","created":"2023-02-21","cited_by_count":8,"area":null},{"id":"2408.04643","title":"Risks, Causes, and Mitigations of Widespread Deployments of Large Language Models (LLMs): A Survey","created":"2024-08-01","cited_by_count":8,"area":null},{"id":"2512.21720","title":"An Information Theoretic Perspective on Agentic System Design","created":"2025-12-25","cited_by_count":8,"area":"agents"},{"id":"2209.06954","title":"Correlation Information Bottleneck: Towards Adapting Pretrained Multimodal Models for Robust Visual Question Answering","created":"2022-09-14","cited_by_count":7,"area":"mllm"},{"id":"2306.15164","title":"DSRM: Boost Textual Adversarial Training with Distribution Shift Risk Minimization","created":"2023-06-27","cited_by_count":7,"area":null},{"id":"2401.12461","title":"Fast Adversarial Training against Textual Adversarial Attacks","created":"2024-01-23","cited_by_count":7,"area":null},{"id":"2210.09658","title":"ROSE: Robust Selective Fine-tuning for Pre-trained Language Models","created":"2022-10-18","cited_by_count":6,"area":null},{"id":"2211.07263","title":"Efficient Adversarial Training with Robust Early-Bird Tickets","created":"2022-11-14","cited_by_count":5,"area":null},{"id":"2303.01239","title":"MixPHM: Redundancy-Aware Parameter-Efficient Tuning for Low-Resource Visual Question Answering","created":"2023-03-02","cited_by_count":5,"area":"mllm"},{"id":"2310.16955","title":"Break it, Imitate it, Fix it: Robustness by Generating Human-Like Attacks","created":"2023-10-25","cited_by_count":5,"area":null},{"id":"2401.03514","title":"ROIC-DM: Robust Text Inference and Classification via Diffusion Model","created":"2024-01-07","cited_by_count":5,"area":"diffusion"},{"id":"2408.05874","title":"LLM-Based Robust Product Classification in Commerce and Compliance","created":"2024-08-11","cited_by_count":5,"area":null},{"id":"2410.23182","title":"ProTransformer: Robustify Transformers via Plug-and-Play Paradigm","created":"2024-10-30","cited_by_count":5,"area":null},{"id":"2501.09804","title":"Enhancing Generalization in Chain of Thought Reasoning for Smaller Models","created":"2025-01-16","cited_by_count":5,"area":"lr"},{"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":"2111.13301","title":"Simple Contrastive Representation Adversarial Learning for NLP Tasks","created":"2021-11-26","cited_by_count":4,"area":null},{"id":"2201.08555","title":"Identifying Adversarial Attacks on Text Classifiers","created":"2022-01-21","cited_by_count":4,"area":null},{"id":"2202.08048","title":"Decorrelate Irrelevant, Purify Relevant: Overcome Textual Spurious Correlations from a Feature Perspective","created":"2022-02-16","cited_by_count":4,"area":null},{"id":"2204.13309","title":"Improving robustness of language models from a geometry-aware perspective","created":"2022-04-28","cited_by_count":4,"area":null},{"id":"2209.01824","title":"A Survey on Measuring and Mitigating Reasoning Shortcuts in Machine Reading Comprehension","created":"2022-09-05","cited_by_count":4,"area":null},{"id":"2210.14957","title":"Disentangled Text Representation Learning with Information-Theoretic Perspective for Adversarial Robustness","created":"2022-10-26","cited_by_count":4,"area":null},{"id":"2301.01142","title":"Mutual Information Regularization for Vertical Federated Learning","created":"2023-01-01","cited_by_count":4,"area":"safety"},{"id":"2309.08316","title":"How to Handle Different Types of Out-of-Distribution Scenarios in Computational Argumentation? A Comprehensive and Fine-Grained Field Study","created":"2023-09-15","cited_by_count":4,"area":null},{"id":"2310.14211","title":"LUNA: A Model-Based Universal Analysis Framework for Large Language Models","created":"2023-10-22","cited_by_count":4,"area":null},{"id":"2507.00981","title":"Evaluating Robustness of Monocular Depth Estimation with Procedural Scene Perturbations","created":"2025-07-01","cited_by_count":4,"area":null},{"id":"2310.14001","title":"Toward Stronger Textual Attack Detectors","created":"2023-10-21","cited_by_count":3,"area":null},{"id":"2312.13933","title":"Structured Probabilistic Coding","created":"2023-12-21","cited_by_count":3,"area":null},{"id":"2402.18825","title":"Utilizing Local Hierarchy with Adversarial Training for Hierarchical Text Classification","created":"2024-02-29","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":"2210.12233","title":"TCAB: A Large-Scale Text Classification Attack Benchmark","created":"2022-10-21","cited_by_count":2,"area":null},{"id":"2212.09272","title":"Statistical Dataset Evaluation: Reliability, Difficulty, and Validity","created":"2022-12-19","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":"2501.02654","title":"Tougher Text, Smarter Models: Raising the Bar for Adversarial Defence Benchmarks","created":"2025-01-05","cited_by_count":2,"area":null},{"id":"2505.19397","title":"Are Time-Series Foundation Models Deployment-Ready? A Systematic Study of Adversarial Robustness Across Domains","created":"2025-05-26","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":"2105.03791","title":"Enhancing Transformers with Gradient Boosted Decision Trees for NLI Fine-Tuning","created":"2021-05-08","cited_by_count":1,"area":null},{"id":"2306.12069","title":"Modeling Hierarchical Reasoning Chains by Linking Discourse Units and Key Phrases for Reading Comprehension","created":"2023-06-21","cited_by_count":1,"area":"lr"},{"id":"2307.01225","title":"Alert-ME: An Explainability-Driven Defense Against Adversarial Examples in Transformer-Based Text Classification","created":"2023-07-03","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":"2311.03955","title":"Elastic Information Bottleneck","created":"2023-11-07","cited_by_count":1,"area":null},{"id":"2403.16176","title":"Subspace Defense: Discarding Adversarial Perturbations by Learning a Subspace for Clean Signals","created":"2024-03-24","cited_by_count":1,"area":null},{"id":"2407.00248","title":"DiffuseDef: Improved Robustness to Adversarial Attacks via Iterative Denoising","created":"2024-06-28","cited_by_count":1,"area":null},{"id":"2507.10330","title":"Bridging Robustness and Generalization Against Word Substitution Attacks in NLP via the Growth Bound Matrix Approach","created":"2025-07-14","cited_by_count":1,"area":null},{"id":"2106.08898","title":"RefBERT: Compressing BERT by Referencing to Pre-computed Representations","created":"2021-06-11","cited_by_count":0,"area":null},{"id":"2202.13636","title":"Improving Lexical Embeddings for Robust Question Answering","created":"2022-02-28","cited_by_count":0,"area":null},{"id":"2304.12764","title":"Test-Time Adaptation with Perturbation Consistency Learning","created":"2023-04-25","cited_by_count":0,"area":null},{"id":"2307.01488","title":"SCAT: Robust Self-supervised Contrastive Learning via Adversarial Training for Text Classification","created":"2023-07-04","cited_by_count":0,"area":null},{"id":"2308.07772","title":"MOLE: MOdular Learning FramEwork via Mutual Information Maximization","created":"2023-08-15","cited_by_count":0,"area":null},{"id":"2404.05088","title":"How much reliable is ChatGPT's prediction on Information Extraction under Input Perturbations?","created":"2024-04-07","cited_by_count":0,"area":null},{"id":"2410.12759","title":"Unitary Multi-Margin BERT for Robust Natural Language Processing","created":"2024-10-16","cited_by_count":0,"area":null},{"id":"2412.16254","title":"Adversarial Robustness through Dynamic Ensemble Learning","created":"2024-12-20","cited_by_count":0,"area":null},{"id":"2503.16550","title":"Unified Enhancement of the Generalization and Robustness of Language Models via Bi-Stage Optimization","created":"2025-03-19","cited_by_count":0,"area":null},{"id":"2505.20933","title":"Information-Theoretic Complementary Prompts for Improved Continual Text Classification","created":"2025-05-27","cited_by_count":0,"area":null},{"id":"2510.00517","title":"Understanding Sensitivity of Differential Attention through the Lens of Adversarial Robustness","created":"2025-10-01","cited_by_count":0,"area":null},{"id":"2606.13439","title":"S-GBT: Smooth Growth Bound Tensor for Certified Robustness Against Word Substitution Attacks in NLP","created":"2026-06-11","cited_by_count":0,"area":null},{"id":"2608.08791","title":"Unsure but Certain: Uncovering the Representation-Confidence Gap in Diffusion Language Models","created":"2026-08-09","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}