{"total":43,"rows":[{"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":"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":"2107.00440","title":"CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding","created":"2021-07-01","cited_by_count":50,"area":null},{"id":"2104.08731","title":"Can NLI Models Verify QA Systems' Predictions?","created":"2021-04-18","cited_by_count":46,"area":null},{"id":"2106.00149","title":"HiddenCut: Simple Data Augmentation for Natural Language Understanding with Better Generalization","created":"2021-05-31","cited_by_count":32,"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":"2102.07818","title":"Certified Robustness to Programmable Transformations in LSTMs","created":"2021-02-15","cited_by_count":18,"area":null},{"id":"2210.16079","title":"Debiasing Masks: A New Framework for Shortcut Mitigation in NLU","created":"2022-10-28","cited_by_count":16,"area":null},{"id":"2203.03047","title":"Recent Advances in Neural Text Generation: A Task-Agnostic Survey","created":"2022-03-06","cited_by_count":14,"area":null},{"id":"2105.12932","title":"Contrastive Fine-tuning Improves Robustness for Neural Rankers","created":"2021-05-27","cited_by_count":12,"area":null},{"id":"2112.10424","title":"Unifying Model Explainability and Robustness for Joint Text Classification and Rationale Extraction","created":"2021-12-20","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":"2306.02842","title":"Improving Conversational Recommendation Systems via Counterfactual Data Simulation","created":"2023-06-05","cited_by_count":10,"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":"2204.10192","title":"Residue-Based Natural Language Adversarial Attack Detection","created":"2022-04-17","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":"2401.18001","title":"Desiderata for the Context Use of Question Answering Systems","created":"2024-01-31","cited_by_count":7,"area":null},{"id":"2104.07646","title":"Are Multilingual BERT models robust? A Case Study on Adversarial Attacks for Multilingual Question Answering","created":"2021-04-15","cited_by_count":6,"area":null},{"id":"2308.11284","title":"LEAP: Efficient and Automated Test Method for NLP Software","created":"2023-08-22","cited_by_count":6,"area":null},{"id":"2109.05793","title":"Virtual Data Augmentation: A Robust and General Framework for Fine-tuning Pre-trained Models","created":"2021-09-13","cited_by_count":5,"area":null},{"id":"2102.11584","title":"Enhancing Model Robustness By Incorporating Adversarial Knowledge Into Semantic Representation","created":"2021-02-23","cited_by_count":4,"area":null},{"id":"2112.09658","title":"Reasoning Chain Based Adversarial Attack for Multi-hop Question Answering","created":"2021-12-17","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":"2302.00094","title":"The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models","created":"2023-01-31","cited_by_count":4,"area":null},{"id":"2101.07942","title":"Towards Confident Machine Reading Comprehension","created":"2021-01-20","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":"2102.03016","title":"Model Agnostic Answer Reranking System for Adversarial Question Answering","created":"2021-02-05","cited_by_count":2,"area":null},{"id":"2110.11987","title":"Improving Robustness of Malware Classifiers using Adversarial Strings Generated from Perturbed Latent Representations","created":"2021-10-22","cited_by_count":2,"area":null},{"id":"2302.11412","title":"Data Augmentation for Neural NLP","created":"2023-02-22","cited_by_count":2,"area":null},{"id":"2309.09380","title":"Mitigating Shortcuts in Language Models with Soft Label Encoding","created":"2023-09-17","cited_by_count":2,"area":null},{"id":"2403.05365","title":"The Impact of Quantization on the Robustness of Transformer-based Text Classifiers","created":"2024-03-08","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":"2110.13656","title":"CLLD: Contrastive Learning with Label Distance for Text Classification","created":"2021-10-25","cited_by_count":1,"area":null},{"id":"2202.11865","title":"Using calibrator to improve robustness in Machine Reading Comprehension","created":"2022-02-24","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":"2207.00187","title":"An Understanding-Oriented Robust Machine Reading Comprehension Model","created":"2022-07-01","cited_by_count":0,"area":null},{"id":"2304.03145","title":"Evaluating the Robustness of Machine Reading Comprehension Models to Low Resource Entity Renaming","created":"2023-04-06","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":"2310.13099","title":"No offence, Bert -- I insult only humans! Multiple addressees sentence-level attack on toxicity detection neural network","created":"2023-10-19","cited_by_count":0,"area":null},{"id":"2310.19403","title":"A Lightweight Method to Generate Unanswerable Questions in English","created":"2023-10-30","cited_by_count":0,"area":null},{"id":"2409.10997","title":"Contextual Breach: Assessing the Robustness of Transformer-based QA Models","created":"2024-09-17","cited_by_count":0,"area":null},{"id":"2409.19766","title":"Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology","created":"2024-09-29","cited_by_count":0,"area":null},{"id":"2602.18800","title":"Operational Robustness of LLMs on Code Generation","created":"2026-02-21","cited_by_count":0,"area":null}],"next_cursor":null}