{"total":62,"rows":[{"id":"2112.08313","title":"Measure and Improve Robustness in NLP Models: A Survey","created":"2021-12-15","cited_by_count":107,"area":null},{"id":"2206.04301","title":"Unveiling Transformers with LEGO: a synthetic reasoning task","created":"2022-06-09","cited_by_count":106,"area":null},{"id":"2310.11589","title":"Eliciting Human Preferences with Language Models","created":"2023-10-17","cited_by_count":92,"area":null},{"id":"2206.14754","title":"Distilling Model Failures as Directions in Latent Space","created":"2022-06-29","cited_by_count":82,"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":"2211.08073","title":"GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-distribution Generalization Perspective","created":"2022-11-15","cited_by_count":72,"area":null},{"id":"2203.05818","title":"ZIN: When and How to Learn Invariance Without Environment Partition?","created":"2022-03-11","cited_by_count":58,"area":null},{"id":"2106.12108","title":"Near-Optimal Linear Regression under Distribution Shift","created":"2021-06-23","cited_by_count":42,"area":null},{"id":"2402.12715","title":"The Clever Hans Mirage: A Comprehensive Survey on Spurious Correlations in Machine Learning","created":"2024-02-20","cited_by_count":41,"area":null},{"id":"2207.09504","title":"Invariant Feature Learning for Generalized Long-Tailed Classification","created":"2022-07-19","cited_by_count":34,"area":null},{"id":"2204.08491","title":"Active Learning Helps Pretrained Models Learn the Intended Task","created":"2022-04-18","cited_by_count":33,"area":null},{"id":"2106.15231","title":"Exploring the Efficacy of Automatically Generated Counterfactuals for Sentiment Analysis","created":"2021-06-29","cited_by_count":30,"area":null},{"id":"2202.05189","title":"Understanding Rare Spurious Correlations in Neural Networks","created":"2022-02-10","cited_by_count":27,"area":null},{"id":"2203.12918","title":"A Rationale-Centric Framework for Human-in-the-loop Machine Learning","created":"2022-03-24","cited_by_count":27,"area":null},{"id":"2112.00071","title":"What to Learn, and How: Toward Effective Learning from Rationales","created":"2021-11-30","cited_by_count":25,"area":null},{"id":"2210.12365","title":"NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation","created":"2022-10-22","cited_by_count":23,"area":null},{"id":"2109.05213","title":"Uncovering Main Causalities for Long-tailed Information Extraction","created":"2021-09-11","cited_by_count":21,"area":null},{"id":"2103.02477","title":"Regularizing towards Causal Invariance: Linear Models with Proxies","created":"2021-03-03","cited_by_count":20,"area":null},{"id":"2108.00968","title":"Robust Semantic Segmentation with Superpixel-Mix","created":"2021-08-02","cited_by_count":19,"area":null},{"id":"2106.05894","title":"Synthesizing Adversarial Negative Responses for Robust Response Ranking and Evaluation","created":"2021-06-10","cited_by_count":16,"area":null},{"id":"2305.17804","title":"Targeted Data Generation: Finding and Fixing Model Weaknesses","created":"2023-05-28","cited_by_count":15,"area":null},{"id":"2209.15177","title":"Domain Generalization -- A Causal Perspective","created":"2022-09-30","cited_by_count":14,"area":null},{"id":"2204.07408","title":"Towards Fine-grained Causal Reasoning and QA","created":"2022-04-15","cited_by_count":13,"area":"lr"},{"id":"2110.00911","title":"Enhancing Model Robustness and Fairness with Causality: A Regularization Approach","created":"2021-10-03","cited_by_count":11,"area":null},{"id":"2401.01259","title":"Do Concept Bottleneck Models Respect Localities?","created":"2024-01-02","cited_by_count":10,"area":null},{"id":"2211.01914","title":"FedGen: Generalizable Federated Learning for Sequential Data","created":"2022-11-03","cited_by_count":9,"area":null},{"id":"2212.08378","title":"Feature Dropout: Revisiting the Role of Augmentations in Contrastive Learning","created":"2022-12-16","cited_by_count":9,"area":null},{"id":"2407.14974","title":"Out of Spuriousity: Improving Robustness to Spurious Correlations without Group Annotations","created":"2024-07-20","cited_by_count":9,"area":null},{"id":"2206.12608","title":"Adversarial Self-Attention for Language Understanding","created":"2022-06-25","cited_by_count":8,"area":null},{"id":"2209.08799","title":"Probing Spurious Correlations in Popular Event-Based Rumor Detection Benchmarks","created":"2022-09-19","cited_by_count":7,"area":null},{"id":"2310.17146","title":"Counterfactual-Augmented Importance Sampling for Semi-Offline Policy Evaluation","created":"2023-10-26","cited_by_count":7,"area":null},{"id":"2310.11991","title":"Removing Spurious Concepts from Neural Network Representations via Joint Subspace Estimation","created":"2023-10-18","cited_by_count":6,"area":null},{"id":"2106.03831","title":"Counterfactual Maximum Likelihood Estimation for Training Deep Networks","created":"2021-06-07","cited_by_count":5,"area":null},{"id":"2206.11673","title":"Is your model predicting the past?","created":"2022-06-23","cited_by_count":5,"area":null},{"id":"2404.01921","title":"A Rationale-centric Counterfactual Data Augmentation Method for Cross-Document Event Coreference Resolution","created":"2024-04-02","cited_by_count":5,"area":null},{"id":"2407.01316","title":"Evaluating Model Performance Under Worst-case Subpopulations","created":"2024-07-01","cited_by_count":5,"area":null},{"id":"2407.05594","title":"SLIM: Spuriousness Mitigation with Minimal Human Annotations","created":"2024-07-08","cited_by_count":5,"area":null},{"id":"2103.13905","title":"StyleLess layer: Improving robustness for real-world driving","created":"2021-03-25","cited_by_count":4,"area":null},{"id":"2209.07879","title":"Less is Better: Recovering Intended-Feature Subspace to Robustify NLU Models","created":"2022-09-16","cited_by_count":4,"area":null},{"id":"2303.17595","title":"Neglected Free Lunch -- Learning Image Classifiers Using Annotation Byproducts","created":"2023-03-30","cited_by_count":4,"area":null},{"id":"2502.05098","title":"TIF: Learning Temporal Invariance in Android Malware Detectors","created":"2025-02-07","cited_by_count":4,"area":null},{"id":"2210.17238","title":"Pneg: Prompt-based Negative Response Generation for Dialogue Response Selection Task","created":"2022-10-31","cited_by_count":3,"area":null},{"id":"2305.05087","title":"Large-Scale Study of Temporal Shift in Health Insurance Claims","created":"2023-05-08","cited_by_count":3,"area":null},{"id":"2305.08524","title":"Measuring Consistency in Text-based Financial Forecasting Models","created":"2023-05-15","cited_by_count":3,"area":null},{"id":"2503.17717","title":"BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors","created":"2025-03-22","cited_by_count":3,"area":null},{"id":"2110.13223","title":"Identifying and Benchmarking Natural Out-of-Context Prediction Problems","created":"2021-10-25","cited_by_count":2,"area":null},{"id":"2403.06424","title":"Bridging Domains with Approximately Shared Features","created":"2024-03-11","cited_by_count":2,"area":null},{"id":"2506.11402","title":"LoRA Users Beware: A Few Spurious Tokens Can Manipulate Your Finetuned Model","created":"2025-06-13","cited_by_count":2,"area":null},{"id":"2510.19640","title":"Latent Space Factorization in LoRA","created":"2025-10-22","cited_by_count":2,"area":null},{"id":"2510.19784","title":"Environment Inference for Learning Generalizable Dynamical System","created":"2025-10-22","cited_by_count":2,"area":null},{"id":"2210.02407","title":"The Influence of Explainable Artificial Intelligence: Nudging Behaviour or Boosting Capability?","created":"2022-10-05","cited_by_count":1,"area":null},{"id":"2305.14104","title":"Out-of-Distribution Generalization in Text Classification: Past, Present, and Future","created":"2023-05-23","cited_by_count":1,"area":null},{"id":"2310.05502","title":"XAL: EXplainable Active Learning Makes Classifiers Better Low-resource Learners","created":"2023-10-09","cited_by_count":1,"area":null},{"id":"2311.01655","title":"Detecting Spurious Correlations via Robust Visual Concepts in Real and AI-Generated Image Classification","created":"2023-11-03","cited_by_count":1,"area":null},{"id":"2504.15686","title":"Invariant Learning with Annotation-free Environments","created":"2025-04-22","cited_by_count":1,"area":null},{"id":"2202.10668","title":"Automatically Generating Counterfactuals for Relation Classification","created":"2022-02-22","cited_by_count":0,"area":null},{"id":"2406.18901","title":"Autoencoder based approach for the mitigation of spurious correlations","created":"2024-06-27","cited_by_count":0,"area":null},{"id":"2502.18353","title":"DBR: Divergence-Based Regularization for Debiasing Natural Language Understanding Models","created":"2025-02-25","cited_by_count":0,"area":null},{"id":"2507.12105","title":"Out-of-distribution data supervision towards biomedical semantic segmentation","created":"2025-07-16","cited_by_count":0,"area":null},{"id":"2603.15158","title":"Point-Identification of a Robust Predictor Under Latent Shift with Imperfect Proxies","created":"2026-03-16","cited_by_count":0,"area":null},{"id":"2605.02658","title":"Deciphering Shortcut Learning from an Evolutionary Game Theory Perspective","created":"2026-05-04","cited_by_count":0,"area":null},{"id":"2605.13340","title":"Shortcut Mitigation via Spurious-Positive Samples","created":"2026-05-13","cited_by_count":0,"area":null}],"next_cursor":null}