Who cites «Evaluating Models' Local Decision Boundaries via Contrast Sets» · page 2

264 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. ← the card

How Much Consistency Is Your Accuracy Worth? — 2023 · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP
Hypothetical Training for Robust Machine Reading Comprehension of Tabular Context — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation — 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023
NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation — 2023 · Northern European Journal of Language Technology
NormBank: A Knowledge Bank of Situational Social Norms — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
On Degrees of Freedom in Defining and Testing Natural Language Understanding — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
REFER: An End-to-end Rationale Extraction Framework for Explanation Regularization — 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)
ReCode: Robustness Evaluation of Code Generation Models — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks — 2023 · arXiv:2307.02477
Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity — 2023 · arXiv:2301.12867
SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples — 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning — 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models — 2023 · arXiv:2311.08370
Sociodemographic Bias in Language Models: A Survey and Forward Path — 2023 · arXiv:2306.08158
SpaceNLI: Evaluating the Consistency of Predicting Inferences In Space — 2023 · Proceedings of the 4th Natural Logic Meets Machine Learning Workshop
Stubborn Lexical Bias in Data and Models — 2023 · Findings of the Association for Computational Linguistics: ACL 2023
The Trickle-down Impact of Reward (In-)consistency on RLHF — 2023 · arXiv:2309.16155
XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models — 2023 · arXiv:2308.01263
(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models'' Performance — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
ACT-Thor: A Controlled Benchmark for Embodied Action Understanding in Simulated Environments — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
ANLIzing the Adversarial Natural Language Inference Dataset — 2022 · Proceedings of the Society for Computation in Linguistics 2022
Adaptive Testing and Debugging of NLP Models — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Analyzing Dynamic Adversarial Training Data in the Limit — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Beyond Model Interpretability: On the Faithfulness and Adversarial Robustness of Contrastive Textual Explanations — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models — 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj
CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Can Transformers Reason in Fragments of Natural Language? — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models — 2022 · Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)
CommonsenseQA 2.0: Exposing the Limits of AI through Gamification — 2022 · arXiv:2201.05320
Consistency Training with Virtual Adversarial Discrete Perturbation — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Contrast Sets for Stativity of English Verbs in Context — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Contrastive Data and Learning for Natural Language Processing — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts
Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in Low-Resource English Varieties — 2022 · Proceedings of the First Workshop on NLP applications to field linguistics
Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Cyberbullying Classifiers are Sensitive to Model-Agnostic Perturbations — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
De-Bias for Generative Extraction in Unified NER Task — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Deep Learning for Text Style Transfer: A Survey — 2022 · Computational Linguistics
DuQM: A Chinese Dataset of Linguistically Perturbed Natural Questions for Evaluating the Robustness of Question Matching Models — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
ER-Test: Evaluating Explanation Regularization Methods for Language Models — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Exploring The Landscape of Distributional Robustness for Question Answering Models — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Exposing the Limits of Video-Text Models through Contrast Sets — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Generalization Differences between End-to-End and Neuro-Symbolic Vision-Language Reasoning Systems — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
HateCheckHIn: Evaluating Hindi Hate Speech Detection Models — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
Hatemoji: A Test Suite and Adversarially-Generated Dataset for Benchmarking and Detecting Emoji-Based Hate — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Holistic Evaluation of Language Models — 2022 · arXiv:2211.09110 · Published in Transactions on Machine Learning Research (TMLR), 2023
Identifying Implicitly Abusive Remarks about Identity Groups using a Linguistically Informed Approach — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
InferES : A Natural Language Inference Corpus for Spanish Featuring Negation-Based Contrastive and Adversarial Examples — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Investigating the Performance of Transformer-Based NLI Models on Presuppositional Inferences — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Learning Invariant Representation Improves Robustness for MRC Models — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Lexical Generalization Improves with Larger Models and Longer Training — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Machine Reading, Fast and Slow: When Do Models ``Understand'''' Language? — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Measure and Improve Robustness in NLP Models: A Survey — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Mitigating Dataset Artifacts in Natural Language Inference Through Automatic Contextual Data Augmentation and Learning Optimization — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
Multilingual HateCheck: Functional Tests for Multilingual Hate Speech Detection Models — 2022 · Proceedings of the Sixth Workshop on Online Abuse and Harms (WOAH)
NeuroCounterfactuals: Beyond Minimal-Edit Counterfactuals for Richer Data Augmentation — 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022
Perturbations and Subpopulations for Testing Robustness in Token-Based Argument Unit Recognition — 2022 · Proceedings of the 9th Workshop on Argument Mining
Possible Stories: Evaluating Situated Commonsense Reasoning under Multiple Possible Scenarios — 2022 · Proceedings of the 29th International Conference on Computational Linguistics
Prompting GPT-3 To Be Reliable — 2022 · arXiv:2210.09150
ROSE: Robust Selective Fine-tuning for Pre-trained Language Models — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Retrieval-guided Counterfactual Generation for QA — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Rich Knowledge Sources Bring Complex Knowledge Conflicts: Recalibrating Models to Reflect Conflicting Evidence — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
RobustLR: A Diagnostic Benchmark for Evaluating Logical Robustness of Deductive Reasoners — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Semantically Distributed Robust Optimization for Vision-and-Language Inference — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
State-of-the-art generalisation research in NLP: A taxonomy and review — 2022 · arXiv:2210.03050 · Nat Mach Intell 5, 1161-1174 (2023)
Tailor: Generating and Perturbing Text with Semantic Controls — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
The Dangers of Underclaiming: Reasons for Caution When Reporting How NLP Systems Fail — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
The Inefficiency of Language Models in Scholarly Retrieval: An Experimental Walk-through — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
Towards a Unified Multi-Dimensional Evaluator for Text Generation — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
VALSE: A Task-Independent Benchmark for Vision and Language Models Centered on Linguistic Phenomena — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
VALUE: Understanding Dialect Disparity in NLU — our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
What Makes Reading Comprehension Questions Difficult? — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
When Chosen Wisely, More Data Is What You Need: A Universal Sample-Efficient Strategy For Data Augmentation — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
\textitGeneralized but not Robust? Comparing the Effects of Data Modification Methods on Out-of-Domain Generalization and Adversarial Robustness — 2022 · Findings of the Association for Computational Linguistics: ACL 2022
A Multilingual Benchmark for Probing Negation-Awareness with Minimal Pairs — 2021 · Proceedings of the 25th Conference on Computational Natural Language Learning
Active Learning by Acquiring Contrastive Examples — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Are Factuality Checkers Reliable? Adversarial Meta-evaluation of Factuality in Summarization — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
Are NLP Models really able to Solve Simple Math Word Problems? — our card · 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Are Rotten Apples Edible? Challenging Commonsense Inference Ability with Exceptions — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Automatic Generation of Contrast Sets from Scene Graphs: Probing the Compositional Consistency of GQA — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Back to Square One: Artifact Detection, Training and Commonsense Disentanglement in the Winograd Schema — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Benchmarking Machine Reading Comprehension: A Psychological Perspective — 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume
Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
CLINE: Contrastive Learning with Semantic Negative Examples for Natural Language Understanding — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
COM2SENSE: A Commonsense Reasoning Benchmark with Complementary Sentences — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
Can NLI Models Verify QA Systems' Predictions? — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
CausaLM: Causal Model Explanation Through Counterfactual Language Models — 2021 · Computational Linguistics
Causal Augmentation for Causal Sentence Classification — 2021 · Proceedings of the First Workshop on Causal Inference and NLP
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond — 2021 · arXiv:2109.00725
Competency Problems: On Finding and Removing Artifacts in Language Data — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Connecting Attributions and QA Model Behavior on Realistic Counterfactuals — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Contrastive Explanations for Model Interpretability — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Coreference Reasoning in Machine Reading Comprehension — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
CrossVQA: Scalably Generating Benchmarks for Systematically Testing VQA Generalization — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing