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# Who cites «Evaluating Models' Local Decision Boundaries via Contrast Sets»

264 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.mova.elamur.ai/free/en/docs/papers/gardner-2020-evaluating-models-local-decision-boundaries/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/gardner-2020-evaluating-models-local-decision-boundaries-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/gardner-2020-evaluating-models-local-decision-boundaries-3/)

1. [Measuring and Mitigating Shortcut Reliance in Language Models with Probe-Based Representation Entanglement](https://aclanthology.org/2026.acl-srw.59/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
2. [NormWorlds-CF: Solver-Verified Counterfactual Normative Reasoning with Metamorphic-Relation GRPO](https://arxiv.org/abs/2607.03957) *— 2026 · arXiv:2607.03957*
3. [Parallel Universes, Parallel Languages: A Comprehensive Study on LLM-based Multilingual Counterfactual Example Generation](https://aclanthology.org/2026.acl-long.12/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [An Investigation of Robustness of LLMs in Mathematical Reasoning: Benchmarking with Mathematically-Equivalent Transformation of Advanced Mathematical Problems](https://arxiv.org/abs/2508.08833) *— 2025 · arXiv:2508.08833*
5. [Evaluation of Attribution Bias in Generator-Aware Retrieval-Augmented Large Language Models](https://aclanthology.org/2025.findings-acl.1087/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
6. [Found in Translation: Measuring Multilingual LLM Consistency as Simple as Translate then Evaluate](https://aclanthology.org/2025.ijcnlp-long.185/) *— 2025 · Proceedings of the 14th International Joint Conference on Natural Language Processing and the 4th Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics*
7. [LinguaLens: Towards Interpreting Linguistic Mechanisms of Large Language Models via Sparse Auto-Encoder](https://aclanthology.org/2025.emnlp-main.1433/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
8. [MRAG: A Modular Retrieval Framework for Time-Sensitive Question Answering](https://aclanthology.org/2025.findings-emnlp.167/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
9. [NeoQA: Evidence-based Question Answering with Generated News Events](https://aclanthology.org/2025.findings-acl.616/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
10. [On Behalf of the Stakeholders: Trends in NLP Model Interpretability in the Era of LLMs](https://aclanthology.org/2025.naacl-long.29/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
11. [People who frequently use ChatGPT for writing tasks are accurate and robust detectors of AI-generated text](https://aclanthology.org/2025.acl-long.267/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [Revisiting Implicitly Abusive Language Detection: Evaluating LLMs in Zero-Shot and Few-Shot Settings](https://aclanthology.org/2025.coling-main.262/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
13. [SCORE: Systematic COnsistency and Robustness Evaluation for Large Language Models](https://aclanthology.org/2025.naacl-industry.39/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 3: Industry Track)*
14. [Truth or Twist? Optimal Model Selection for Reliable Label Flipping Evaluation in LLM-based Counterfactuals](https://aclanthology.org/2025.inlg-main.5/) *— 2025 · Proceedings of the 18th International Natural Language Generation Conference*
15. [Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://aclanthology.org/2025.acl-long.169/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [reWordBench: Benchmarking and Improving the Robustness of Reward Models with Transformed Inputs](https://aclanthology.org/2025.emnlp-main.167/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
17. [A Peek into Token Bias: Large Language Models Are Not Yet Genuine Reasoners](https://aclanthology.org/2024.emnlp-main.272/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
18. [A Survey on Natural Language Counterfactual Generation](https://aclanthology.org/2024.findings-emnlp.276/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
19. [AppWorld: A Controllable World of Apps and People for Benchmarking Interactive Coding Agents](https://aclanthology.org/2024.acl-long.850/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
20. [Artifacts or Abduction: How Do LLMs Answer Multiple-Choice Questions Without the Question?](https://aclanthology.org/2024.acl-long.555/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
21. [Beyond Agreement: Diagnosing the Rationale Alignment of Automated Essay Scoring Methods based on Linguistically-informed Counterfactuals](https://aclanthology.org/2024.findings-emnlp.520/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
22. [CEval: A Benchmark for Evaluating Counterfactual Text Generation](https://aclanthology.org/2024.inlg-main.6/) *— 2024 · Proceedings of the 17th International Natural Language Generation Conference*
23. [Chaining Event Spans for Temporal Relation Grounding](https://aclanthology.org/2024.eacl-long.101/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [Cutting Through the Noise: Boosting LLM Performance on Math Word Problems](https://md.mova.elamur.ai/free/en/docs/papers/anantheswaran-2024-cutting-through-noise-boosting-llm/) *— our card · 2024 · arXiv:2406.15444*
25. [Desiderata For The Context Use Of Question Answering Systems](https://aclanthology.org/2024.eacl-long.47/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [Efficient Lifelong Model Evaluation in an Era of Rapid Progress](https://arxiv.org/abs/2402.19472) *— 2024 · arXiv:2402.19472*
27. [Evaluating ChatGPT against Functionality Tests for Hate Speech Detection](https://aclanthology.org/2024.lrec-main.564/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
28. [GuardBench: A Large-Scale Benchmark for Guardrail Models](https://aclanthology.org/2024.emnlp-main.1022/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
29. [How Often Are Errors in Natural Language Reasoning Due to Paraphrastic Variability?](https://aclanthology.org/2024.tacl-1.63/) *— 2024 · Transactions of the Association for Computational Linguistics*
30. [Improving Adversarial Data Collection by Supporting Annotators: Lessons from GAHD, a German Hate Speech Dataset](https://aclanthology.org/2024.naacl-long.248/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
31. [LJPCheck: Functional Tests for Legal Judgment Prediction](https://aclanthology.org/2024.findings-acl.350/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
32. [LLMs for Generating and Evaluating Counterfactuals: A Comprehensive Study](https://aclanthology.org/2024.findings-emnlp.870/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
33. [Measuring and Improving Attentiveness to Partial Inputs with Counterfactuals](https://aclanthology.org/2024.findings-emnlp.205/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
34. [NevIR: Negation in Neural Information Retrieval](https://aclanthology.org/2024.eacl-long.139/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
35. [One Thousand and One Pairs: A ``novel'''' challenge for long-context language models](https://aclanthology.org/2024.emnlp-main.948/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
36. [PairCFR: Enhancing Model Training on Paired Counterfactually Augmented Data through Contrastive Learning](https://aclanthology.org/2024.acl-long.646/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
37. [Principles from Clinical Research for NLP Model Generalization](https://aclanthology.org/2024.naacl-long.127/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
38. [RORA: Robust Free-Text Rationale Evaluation](https://aclanthology.org/2024.acl-long.60/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [Seemingly Plausible Distractors in Multi-Hop Reasoning: Are Large Language Models Attentive Readers?](https://aclanthology.org/2024.emnlp-main.147/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
40. [SynthEval: Hybrid Behavioral Testing of NLP Models with Synthetic CheckLists](https://aclanthology.org/2024.findings-emnlp.412/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
41. [The Art of Saying No: Contextual Noncompliance in Language Models](https://arxiv.org/abs/2407.12043) *— 2024 · arXiv:2407.12043*
42. [Unsupervised Contrast-Consistent Ranking with Language Models](https://aclanthology.org/2024.eacl-long.54/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [V-DPO: Mitigating Hallucination in Large Vision Language Models via Vision-Guided Direct Preference Optimization](https://aclanthology.org/2024.findings-emnlp.775/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
44. [Whispers of Doubt Amidst Echoes of Triumph in NLP Robustness](https://aclanthology.org/2024.naacl-long.310/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
45. [A Multilingual Evaluation of NER Robustness to Adversarial Inputs](https://aclanthology.org/2023.repl4nlp-1.4/) *— 2023 · Proceedings of the 8th Workshop on Representation Learning for NLP (RepL4NLP 2023)*
46. [A Pretrainer''s Guide to Training Data: Measuring the Effects of Data Age, Domain Coverage, Quality, & Toxicity](https://arxiv.org/abs/2305.13169) *— 2023 · arXiv:2305.13169*
47. [An Overview Of Temporal Commonsense Reasoning and Acquisition](https://arxiv.org/abs/2308.00002) *— 2023 · arXiv:2308.00002*
48. [Benchmarking Long-tail Generalization with Likelihood Splits](https://aclanthology.org/2023.findings-eacl.71/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
49. [COBRA Frames: Contextual Reasoning about Effects and Harms of Offensive Statements](https://aclanthology.org/2023.findings-acl.392/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
50. [COLA: Contextualized Commonsense Causal Reasoning from the Causal Inference Perspective](https://aclanthology.org/2023.acl-long.288/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
51. [CREST: A Joint Framework for Rationalization and Counterfactual Text Generation](https://aclanthology.org/2023.acl-long.842/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
52. [Can NLI Provide Proper Indirect Supervision for Low-resource Biomedical Relation Extraction?](https://aclanthology.org/2023.acl-long.138/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
53. [Chain-of-Questions Training with Latent Answers for Robust Multistep Question Answering](https://aclanthology.org/2023.emnlp-main.547/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
54. [Cold-Start Data Selection for Better Few-shot Language Model Fine-tuning: A Prompt-based Uncertainty Propagation Approach](https://aclanthology.org/2023.acl-long.141/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
55. [Counterfactual Active Learning for Out-of-Distribution Generalization](https://aclanthology.org/2023.acl-long.636/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
56. [DISCO: Distilling Counterfactuals with Large Language Models](https://aclanthology.org/2023.acl-long.302/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
57. [Data Curation Alone Can Stabilize In-context Learning](https://aclanthology.org/2023.acl-long.452/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
58. [Deductive Additivity for Planning of Natural Language Proofs](https://aclanthology.org/2023.nlrse-1.11/) *— 2023 · Proceedings of the 1st Workshop on Natural Language Reasoning and Structured Explanations (NLRSE)*
59. [DistillCSE: Distilled Contrastive Learning for Sentence Embeddings](https://aclanthology.org/2023.findings-emnlp.547/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
60. [Do Pretrained Contextual Language Models Distinguish between Hebrew Homograph Analyses?](https://md.mova.elamur.ai/free/en/docs/papers/eacl-2023-shmidman-do-pretrained-contextual-language-models/) *— our card · 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
61. [EXPLAIN, EDIT, GENERATE: Rationale-Sensitive Counterfactual Data Augmentation for Multi-hop Fact Verification](https://aclanthology.org/2023.emnlp-main.826/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
62. [Euphemistic Abuse – A New Dataset and Classification Experiments for Implicitly Abusive Language](https://aclanthology.org/2023.emnlp-main.1012/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
63. [Evaluating the Rationale Understanding of Critical Reasoning in Logical Reading Comprehension](https://aclanthology.org/2023.emnlp-main.9/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
64. [Exploring Contrast Consistency of Open-Domain Question Answering Systems on Minimally Edited Questions](https://aclanthology.org/2023.tacl-1.61/) *— 2023 · Transactions of the Association for Computational Linguistics*
65. [Fighting Bias With Bias: Promoting Model Robustness by Amplifying Dataset Biases](https://aclanthology.org/2023.findings-acl.833/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
66. [GLUE-X: Evaluating Natural Language Understanding Models from an Out-of-Distribution Generalization Perspective](https://aclanthology.org/2023.findings-acl.806/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
67. [Improving Classifier Robustness through Active Generative Counterfactual Data Augmentation](https://aclanthology.org/2023.findings-emnlp.10/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
68. [NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation](https://nejlt.ep.liu.se/article/view/4725) *— 2023 · Northern European Journal of Language Technology*
69. [NormBank: A Knowledge Bank of Situational Social Norms](https://aclanthology.org/2023.acl-long.429/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
70. [Out-of-Distribution Generalization in Natural Language Processing: Past, Present, and Future](https://aclanthology.org/2023.emnlp-main.276/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
71. [People Make Better Edits: Measuring the Efficacy of LLM-Generated Counterfactually Augmented Data for Harmful Language Detection](https://aclanthology.org/2023.emnlp-main.649/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
72. [ReCode: Robustness Evaluation of Code Generation Models](https://aclanthology.org/2023.acl-long.773/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
73. [Reasoning or Reciting? Exploring the Capabilities and Limitations of Language Models Through Counterfactual Tasks](https://arxiv.org/abs/2307.02477) *— 2023 · arXiv:2307.02477*
74. [Red teaming ChatGPT via Jailbreaking: Bias, Robustness, Reliability and Toxicity](https://arxiv.org/abs/2301.12867) *— 2023 · arXiv:2301.12867*
75. [SCENE: Self-Labeled Counterfactuals for Extrapolating to Negative Examples](https://aclanthology.org/2023.emnlp-main.485/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
76. [ScoNe: Benchmarking Negation Reasoning in Language Models With Fine-Tuning and In-Context Learning](https://aclanthology.org/2023.acl-short.154/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
77. [SimpleSafetyTests: a Test Suite for Identifying Critical Safety Risks in Large Language Models](https://arxiv.org/abs/2311.08370) *— 2023 · arXiv:2311.08370*
78. [Sociodemographic Bias in Language Models: A Survey and Forward Path](https://arxiv.org/abs/2306.08158) *— 2023 · arXiv:2306.08158*
79. [Stubborn Lexical Bias in Data and Models](https://aclanthology.org/2023.findings-acl.516/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
80. [The Trickle-down Impact of Reward (In-)consistency on RLHF](https://arxiv.org/abs/2309.16155) *— 2023 · arXiv:2309.16155*
81. [XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models](https://arxiv.org/abs/2308.01263) *— 2023 · arXiv:2308.01263*
82. [(Un)solving Morphological Inflection: Lemma Overlap Artificially Inflates Models'' Performance](https://aclanthology.org/2022.acl-short.96/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
83. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
84. [Adaptive Testing and Debugging of NLP Models](https://aclanthology.org/2022.acl-long.230/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
85. [Analyzing Dynamic Adversarial Training Data in the Limit](https://aclanthology.org/2022.findings-acl.18/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
86. [AutoCAD: Automatically Generate Counterfactuals for Mitigating Shortcut Learning](https://aclanthology.org/2022.findings-emnlp.170/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
87. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
88. [CONDAQA: A Contrastive Reading Comprehension Dataset for Reasoning about Negation](https://aclanthology.org/2022.emnlp-main.598/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
89. [CORE: A Retrieve-then-Edit Framework for Counterfactual Data Generation](https://aclanthology.org/2022.findings-emnlp.216/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
90. [Can Transformers Reason in Fragments of Natural Language?](https://aclanthology.org/2022.emnlp-main.768/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
91. [Cascading Biases: Investigating the Effect of Heuristic Annotation Strategies on Data and Models](https://aclanthology.org/2022.emnlp-main.438/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
92. [Challenges in Applying Explainability Methods to Improve the Fairness of NLP Models](https://aclanthology.org/2022.trustnlp-1.8/) *— 2022 · Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)*
93. [CommonsenseQA 2.0: Exposing the Limits of AI through Gamification](https://arxiv.org/abs/2201.05320) *— 2022 · arXiv:2201.05320*
94. [Consistency Training with Virtual Adversarial Discrete Perturbation](https://aclanthology.org/2022.naacl-main.414/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
95. [Contrast Sets for Stativity of English Verbs in Context](https://aclanthology.org/2022.coling-1.354/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
96. [Contrastive Data and Learning for Natural Language Processing](https://aclanthology.org/2022.naacl-tutorials.6/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts*
97. [Corpus-Guided Contrast Sets for Morphosyntactic Feature Detection in Low-Resource English Varieties](https://aclanthology.org/2022.fieldmatters-1.2/) *— 2022 · Proceedings of the First Workshop on NLP applications to field linguistics*
98. [Counterfactually Augmented Data and Unintended Bias: The Case of Sexism and Hate Speech Detection](https://aclanthology.org/2022.naacl-main.347/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
99. [De-Bias for Generative Extraction in Unified NER Task](https://aclanthology.org/2022.acl-long.59/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
100. [Deep Learning for Text Style Transfer: A Survey](https://aclanthology.org/2022.cl-1.6/) *— 2022 · Computational Linguistics*

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