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# Who cites «Adversarial Examples for Evaluating Reading Comprehension Systems»

579 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/jia-2017-adversarial-examples-evaluating-reading-comprehension/)

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1. [Beyond Static Synthetic Noise: Assessing the Robustness of Large Language Models to Natural Context Variation in the Real World](https://aclanthology.org/2026.findings-acl.1796/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
2. [DAMASHA: Detecting AI in Mixed Adversarial Texts via Segmentation with Human-interpretable Attribution](https://aclanthology.org/2026.findings-eacl.326/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
3. [Do Images Speak Louder than Words? Investigating the Effect of Textual Misinformation in VLMs](https://aclanthology.org/2026.eacl-long.323/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [FLUKE: A Linguistically-Driven and Task-Agnostic Framework for Robustness Evaluation](https://aclanthology.org/2026.findings-eacl.269/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
5. [GSM-Noise: Exploring and Enhancing Large Language Models'' Reasoning under Noisy Inputs](https://aclanthology.org/2026.findings-acl.1748/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
6. [Look Within or Beyond? A Theoretical Comparison Between Parameter-Efficient and Full Fine-Tuning](https://aclanthology.org/2026.acl-long.2208/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [Moneyball with LLMs: Analyzing Tabular Summarization in Sports Narratives](https://aclanthology.org/2026.findings-acl.2072/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
8. [MultiWikiQA: A Reading Comprehension Benchmark in 300+ Languages](https://aclanthology.org/2026.lrec-1.499/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
9. [Parameter-Efficient Quantum NLP for Paraphrase Detection: Performance, Robustness, and Entanglement](https://arxiv.org/abs/2609.14529) *— 2026 · arXiv:2609.14529*
10. [StanceAttack: Adversarial Attack for Stance Detection](https://aclanthology.org/2026.findings-acl.2034/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
11. [TruthTrap: A Bilingual Benchmark for Evaluating Factually Correct Yet Misleading Information in Question Answering](https://aclanthology.org/2026.findings-eacl.155/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
12. [Uncovering Currency Bias and Syntax Gap in Text Embedding Models](https://aclanthology.org/2026.findings-acl.2118/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
13. [Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions](https://arxiv.org/abs/2504.11373) *— 2025 · arXiv:2504.11373*
14. [Corpus Poisoning via Approximate Greedy Gradient Descent](https://aclanthology.org/2025.findings-acl.222/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
15. [Is your benchmark truly adversarial? AdvScore: Evaluating Human-Grounded Adversarialness](https://aclanthology.org/2025.naacl-long.27/) *— 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)*
16. [Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets](https://aclanthology.org/2025.cl-1.4/) *— 2025 · Computational Linguistics*
17. [Normative Conflicts and Shallow AI Alignment](https://arxiv.org/abs/2506.04679) *— 2025 · arXiv:2506.04679 · Milliere, R. (2025). Normative conflicts and shallow AI alignment. Philosophical Studies, 1-44*
18. [YESciEval: Robust LLM-as-a-Judge for Scientific Question Answering](https://aclanthology.org/2025.acl-long.675/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [A Comprehensive Survey of Contamination Detection Methods in Large Language Models](https://arxiv.org/abs/2404.00699) *— 2024 · arXiv:2404.00699*
20. [Adversarial Math Word Problem Generation](https://aclanthology.org/2024.findings-emnlp.292/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
21. [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)*
22. [CATfOOD: Counterfactual Augmented Training for Improving Out-of-Domain Performance and Calibration](https://aclanthology.org/2024.eacl-long.113/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
23. [Characterizing LLM Abstention Behavior in Science QA with Context Perturbations](https://aclanthology.org/2024.findings-emnlp.197/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
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. [Do LLMs Overcome Shortcut Learning? An Evaluation of Shortcut Challenges in Large Language Models](https://aclanthology.org/2024.emnlp-main.679/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
27. [ECON: On the Detection and Resolution of Evidence Conflicts](https://aclanthology.org/2024.emnlp-main.447/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
28. [Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training](https://aclanthology.org/2024.acl-long.540/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
29. [EvoGrad: A Dynamic Take on the Winograd Schema Challenge with Human Adversaries](https://aclanthology.org/2024.lrec-main.592/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
30. [Explore Spurious Correlations at the Concept Level in Language Models for Text Classification](https://aclanthology.org/2024.acl-long.28/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
31. [Fast Adversarial Attacks on Language Models In One GPU Minute](https://arxiv.org/abs/2402.15570) *— 2024 · arXiv:2402.15570*
32. [Finding a Needle in the Adversarial Haystack: A Targeted Paraphrasing Approach For Uncovering Edge Cases with Minimal Distribution Distortion](https://aclanthology.org/2024.eacl-long.33/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
33. [Instructing Large Language Models to Identify and Ignore Irrelevant Conditions](https://aclanthology.org/2024.naacl-long.379/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
34. [Machine Translation Testing via Syntactic Tree Pruning](https://arxiv.org/abs/2401.00751) *— 2024 · arXiv:2401.00751*
35. [Mass-Editing Memory with Attention in Transformers: A cross-lingual exploration of knowledge](https://aclanthology.org/2024.findings-acl.347/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
36. [Navigating the Shortcut Maze: A Comprehensive Analysis of Shortcut Learning in Text Classification by Language Models](https://aclanthology.org/2024.findings-emnlp.146/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
37. [Predicting Machine Translation Performance on Low-Resource Languages: The Role of Domain Similarity](https://aclanthology.org/2024.findings-eacl.100/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
38. [RE-RAG: Improving Open-Domain QA Performance and Interpretability with Relevance Estimator in Retrieval-Augmented Generation](https://aclanthology.org/2024.emnlp-main.1236/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
39. [Random Smooth-based Certified Defense against Text Adversarial Attack](https://aclanthology.org/2024.findings-eacl.83/) *— 2024 · Findings of the Association for Computational Linguistics: EACL 2024*
40. [ReEval: Automatic Hallucination Evaluation for Retrieval-Augmented Large Language Models via Transferable Adversarial Attacks](https://aclanthology.org/2024.findings-naacl.85/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
41. [Robust and Scalable Model Editing for Large Language Models](https://aclanthology.org/2024.lrec-main.1235/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
42. [The Art of Saying No: Contextual Noncompliance in Language Models](https://arxiv.org/abs/2407.12043) *— 2024 · arXiv:2407.12043*
43. [The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783) *— 2024 · arXiv:2407.21783*
44. [Think Twice: Measuring the Efficiency of Eliminating Prediction Shortcuts of Question Answering Models](https://aclanthology.org/2024.eacl-long.133/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
45. [Two Heads are Better than One: Nested PoE for Robust Defense Against Multi-Backdoors](https://aclanthology.org/2024.naacl-long.40/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
46. [VISREAS: Complex Visual Reasoning with Unanswerable Questions](https://aclanthology.org/2024.findings-acl.402/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
47. [What Evidence Do Language Models Find Convincing?](https://aclanthology.org/2024.acl-long.403/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [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)*
49. [``Knowing When You Don''t Know'''': A Multilingual Relevance Assessment Dataset for Robust Retrieval-Augmented Generation](https://aclanthology.org/2024.findings-emnlp.730/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
50. [A Lightweight Method to Generate Unanswerable Questions in English](https://aclanthology.org/2023.findings-emnlp.491/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
51. [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)*
52. [A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation](https://arxiv.org/abs/2305.11391) *— 2023 · arXiv:2305.11391*
53. [Adversarial Text Generation by Search and Learning](https://aclanthology.org/2023.findings-emnlp.1053/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
54. [An Empirical Survey of Data Augmentation for Limited Data Learning in NLP](https://aclanthology.org/2023.tacl-1.12/) *— 2023 · Transactions of the Association for Computational Linguistics*
55. [Are aligned neural networks adversarially aligned?](https://arxiv.org/abs/2306.15447) *— 2023 · arXiv:2306.15447*
56. [Assessing Out-of-Domain Language Model Performance from Few Examples](https://aclanthology.org/2023.eacl-main.175/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
57. [Automatically Auditing Large Language Models via Discrete Optimization](https://arxiv.org/abs/2303.04381) *— 2023 · arXiv:2303.04381*
58. [BITE: Textual Backdoor Attacks with Iterative Trigger Injection](https://aclanthology.org/2023.acl-long.725/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
59. [CCL23-Eval 任务9总结报告:汉语高考阅读理解对抗鲁棒评测 (Overview of CCL23-Eval Task 9: Adversarial Robustness Evaluation for Chinese Gaokao Reading Comprehension)](https://aclanthology.org/2023.ccl-3.33/) *— 2023 · Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)*
60. [Causal Intervention for Mitigating Name Bias in Machine Reading Comprehension](https://aclanthology.org/2023.findings-acl.812/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
61. [Certified Robustness to Text Adversarial Attacks by Randomized MASK](https://aclanthology.org/2023.cl-2.5/) *— 2023 · Computational Linguistics*
62. [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://arxiv.org/abs/2306.11698) *— 2023 · arXiv:2306.11698*
63. [Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction](https://aclanthology.org/2023.acl-long.354/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
64. [Do Question Answering Modeling Improvements Hold Across Benchmarks?](https://aclanthology.org/2023.acl-long.736/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
65. [Document-Level Event Argument Extraction With a Chain Reasoning Paradigm](https://aclanthology.org/2023.acl-long.532/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
66. [Don''t Retrain, Just Rewrite: Countering Adversarial Perturbations by Rewriting Text](https://aclanthology.org/2023.acl-long.781/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
67. [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*
68. [Guiding LLM to Fool Itself: Automatically Manipulating Machine Reading Comprehension Shortcut Triggers](https://aclanthology.org/2023.findings-emnlp.569/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
69. [HeQ: a Large and Diverse Hebrew Reading Comprehension Benchmark](https://md.mova.elamur.ai/free/en/docs/papers/findings-2023-cohen-heq-large-diverse-hebrew-reading/) *— our card · 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
70. [How Fragile is Relation Extraction under Entity Replacements?](https://aclanthology.org/2023.conll-1.27/) *— 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)*
71. [INVITE: a Testbed of Automatically Generated Invalid Questions to Evaluate Large Language Models for Hallucinations](https://aclanthology.org/2023.findings-emnlp.360/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
72. [Improving Zero-shot Reader by Reducing Distractions from Irrelevant Documents in Open-Domain Question Answering](https://aclanthology.org/2023.findings-emnlp.207/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
73. [Improving the robustness of NLI models with minimax training](https://aclanthology.org/2023.acl-long.801/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
74. [Inferring Capabilities from Task Performance with Bayesian Triangulation](https://arxiv.org/abs/2309.11975) *— 2023 · arXiv:2309.11975*
75. [Jamp: Controlled Japanese Temporal Inference Dataset for Evaluating Generalization Capacity of Language Models](https://aclanthology.org/2023.acl-srw.8/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
76. [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://arxiv.org/abs/2302.00093) *— 2023 · arXiv:2302.00093*
77. [Large Language Models Can be Lazy Learners: Analyze Shortcuts in In-Context Learning](https://aclanthology.org/2023.findings-acl.284/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
78. [Learning the Legibility of Visual Text Perturbations](https://aclanthology.org/2023.eacl-main.238/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
79. [Low-Resource Languages Jailbreak GPT-4](https://arxiv.org/abs/2310.02446) *— 2023 · arXiv:2310.02446*
80. [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://arxiv.org/abs/2310.01558) *— 2023 · arXiv:2310.01558*
81. [MeetingQA: Extractive Question-Answering on Meeting Transcripts](https://aclanthology.org/2023.acl-long.837/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
82. [Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning](https://aclanthology.org/2023.conll-1.20/) *— 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)*
83. [Mitigating Biases in Hate Speech Detection from A Causal Perspective](https://aclanthology.org/2023.findings-emnlp.440/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
84. [Model-tuning Via Prompts Makes NLP Models Adversarially Robust](https://aclanthology.org/2023.emnlp-main.576/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
85. [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*
86. [Noisy Exemplars Make Large Language Models More Robust: A Domain-Agnostic Behavioral Analysis](https://aclanthology.org/2023.emnlp-main.277/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
87. [Post-Abstention: Towards Reliably Re-Attempting the Abstained Instances in QA](https://aclanthology.org/2023.acl-long.55/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [RoMQA: A Benchmark for Robust, Multi-evidence, Multi-answer Question Answering](https://aclanthology.org/2023.findings-emnlp.470/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
89. [Robust Natural Language Understanding with Residual Attention Debiasing](https://aclanthology.org/2023.findings-acl.32/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
90. [RobustGEC: Robust Grammatical Error Correction Against Subtle Context Perturbation](https://aclanthology.org/2023.emnlp-main.1043/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
91. [Similarizing the Influence of Words with Contrastive Learning to Defend Word-level Adversarial Text Attack](https://aclanthology.org/2023.findings-acl.500/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
92. [Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks](https://arxiv.org/abs/2310.10844) *— 2023 · arXiv:2310.10844*
93. [The Earth is Flat because...: Investigating LLMs'' Belief towards Misinformation via Persuasive Conversation](https://arxiv.org/abs/2312.09085) *— 2023 · arXiv:2312.09085*
94. [The Trickle-down Impact of Reward (In-)consistency on RLHF](https://arxiv.org/abs/2309.16155) *— 2023 · arXiv:2309.16155*
95. [Towards Building a Robust Toxicity Predictor](https://aclanthology.org/2023.acl-industry.56/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)*
96. [Translated Benchmarks Can Be Misleading: the Case of Estonian Question Answering](https://aclanthology.org/2023.nodalida-1.71/) *— 2023 · Proceedings of the 24th Nordic Conference on Computational Linguistics (NoDaLiDa)*
97. [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://arxiv.org/abs/2307.15043) *— 2023 · arXiv:2307.15043*
98. [Using contradictions improves question answering systems](https://aclanthology.org/2023.acl-short.72/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
99. [When and Why Does Bias Mitigation Work?](https://aclanthology.org/2023.findings-emnlp.619/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
100. [Which Spurious Correlations Impact Reasoning in NLI Models? A Visual Interactive Diagnosis through Data-Constrained Counterfactuals](https://aclanthology.org/2023.acl-demo.44/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*

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