---
title: "Who cites Adversarial Examples for Evaluating Reading…"
description: "The papers of the Elamur Research library that cite «Adversarial Examples for Evaluating Reading Comprehension Systems», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/2/"
---
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# Who cites «Adversarial Examples for Evaluating Reading Comprehension Systems» · page 2

579 papers of our library cite it, newest first: found in their bibliographies, which we read ourselves. [← the card](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/) · **2** · [3](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/3/) · … · [6](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/cited-by/page/6/)

1. [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)*
2. [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*
3. [Adversarial Text Generation by Search and Learning](https://aclanthology.org/2023.findings-emnlp.1053/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
4. [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*
5. [Analyzing Pre-trained and Fine-tuned Language Models](https://aclanthology.org/2023.bigpicture-1.10/) *— 2023 · Proceedings of the Big Picture Workshop*
6. [Are aligned neural networks adversarially aligned?](https://arxiv.org/abs/2306.15447) *— 2023 · arXiv:2306.15447*
7. [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*
8. [Automatically Auditing Large Language Models via Discrete Optimization](https://arxiv.org/abs/2303.04381) *— 2023 · arXiv:2303.04381*
9. [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)*
10. [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)*
11. [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*
12. [Certified Robustness to Text Adversarial Attacks by Randomized MASK](https://aclanthology.org/2023.cl-2.5/) *— 2023 · Computational Linguistics*
13. [Compressing and Debiasing Vision-Language Pre-Trained Models for Visual Question Answering](https://aclanthology.org/2023.emnlp-main.34/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
14. [Context Quality Matters in Training Fusion-in-Decoder for Extractive Open-Domain Question Answering](https://aclanthology.org/2023.findings-emnlp.784/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
15. [DeSIQ: Towards an Unbiased, Challenging Benchmark for Social Intelligence Understanding](https://aclanthology.org/2023.emnlp-main.191/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
16. [DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models](https://arxiv.org/abs/2306.11698) *— 2023 · arXiv:2306.11698*
17. [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)*
18. [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)*
19. [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)*
20. [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)*
21. [Empirical Investigation of Neural Symbolic Reasoning Strategies](https://aclanthology.org/2023.findings-eacl.86/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
22. [Enhancing Unsupervised Semantic Parsing with Distributed Contextual Representations](https://aclanthology.org/2023.findings-acl.726/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
23. [Evaluating and Enhancing the Robustness of Code Pre-trained Models through Structure-Aware Adversarial Samples Generation](https://aclanthology.org/2023.findings-emnlp.991/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
24. [Explaining Math Word Problem Solvers](https://arxiv.org/abs/2307.13128) *— 2023 · arXiv:2307.13128 · Published in 6th International Conference on Natural Language Processing and Information Retrieval (NLPIR 2022)*
25. [Exploring Automatically Perturbed Natural Language Explanations in Relation Extraction](https://aclanthology.org/2023.findings-acl.214/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
26. [Exploring Schema Generalizability of Text-to-SQL](https://aclanthology.org/2023.findings-acl.87/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
27. [FiRo: Finite-context Indexing of Restricted Output Space for NLP Models Facing Noisy Input](https://aclanthology.org/2023.ijcnlp-main.54/) *— 2023 · Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [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*
29. [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*
30. [HeQ: a Large and Diverse Hebrew Reading Comprehension Benchmark](https://md.research.elamur.ai/free/en/papers/findings-2023-cohen-heq-large-diverse-hebrew-reading/) *— our card · 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
31. [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)*
32. [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*
33. [Implications of Annotation Artifacts in Edge Probing Test Datasets](https://aclanthology.org/2023.conll-1.39/) *— 2023 · Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL)*
34. [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*
35. [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)*
36. [Inferring Capabilities from Task Performance with Bayesian Triangulation](https://arxiv.org/abs/2309.11975) *— 2023 · arXiv:2309.11975*
37. [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)*
38. [Large Language Models Can Be Easily Distracted by Irrelevant Context](https://arxiv.org/abs/2302.00093) *— 2023 · arXiv:2302.00093*
39. [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*
40. [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*
41. [Learning to Ignore Adversarial Attacks](https://aclanthology.org/2023.eacl-main.216/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
42. [Logic Unveils Truth, While Disguise Obscures It: Transition Logic Augmented Response Selection for Multi-Turn Dialogue](https://aclanthology.org/2023.findings-emnlp.513/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
43. [Low-Resource Languages Jailbreak GPT-4](https://arxiv.org/abs/2310.02446) *— 2023 · arXiv:2310.02446*
44. [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://arxiv.org/abs/2310.01558) *— 2023 · arXiv:2310.01558*
45. [Measuring Consistency in Text-based Financial Forecasting Models](https://aclanthology.org/2023.acl-long.769/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
46. [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)*
47. [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)*
48. [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*
49. [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*
50. [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*
51. [NatLogAttack: A Framework for Attacking Natural Language Inference Models with Natural Logic](https://aclanthology.org/2023.acl-long.554/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
52. [No offence, Bert - I insult only humans! Multilingual sentence-level attack on toxicity detection networks](https://aclanthology.org/2023.findings-emnlp.155/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
53. [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*
54. [On Degrees of Freedom in Defining and Testing Natural Language Understanding](https://aclanthology.org/2023.findings-acl.861/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
55. [Pay Attention to the Robustness of Chinese Minority Language Models! Syllable-level Textual Adversarial Attack on Tibetan Script](https://aclanthology.org/2023.trustnlp-1.4/) *— 2023 · Proceedings of the 3rd Workshop on Trustworthy Natural Language Processing (TrustNLP 2023)*
56. [PerturbScore: Connecting Discrete and Continuous Perturbations in NLP](https://aclanthology.org/2023.findings-emnlp.442/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
57. [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)*
58. [PromptAttack: Probing Dialogue State Trackers with Adversarial Prompts](https://aclanthology.org/2023.findings-acl.677/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
59. [Pseudointelligence: A Unifying Lens on Language Model Evaluation](https://aclanthology.org/2023.findings-emnlp.485/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
60. [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*
61. [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*
62. [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*
63. [RobustQA: A Framework for Adversarial Text Generation Analysis on Question Answering Systems](https://aclanthology.org/2023.emnlp-demo.24/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing: System Demonstrations*
64. [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*
65. [Survey of Vulnerabilities in Large Language Models Revealed by Adversarial Attacks](https://arxiv.org/abs/2310.10844) *— 2023 · arXiv:2310.10844*
66. [System Report for CCL23-Eval Task 9: HUST1037 Explore Proper Prompt Strategy for LLM in MRC Task](https://aclanthology.org/2023.ccl-3.34/) *— 2023 · Proceedings of the 22nd Chinese National Conference on Computational Linguistics (Volume 3: Evaluations)*
67. [TextVerifier: Robustness Verification for Textual Classifiers with Certifiable Guarantees](https://aclanthology.org/2023.findings-acl.267/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
68. [The Earth is Flat because...: Investigating LLMs” Belief towards Misinformation via Persuasive Conversation](https://arxiv.org/abs/2312.09085) *— 2023 · arXiv:2312.09085*
69. [The Impacts of Unanswerable Questions on the Robustness of Machine Reading Comprehension Models](https://aclanthology.org/2023.eacl-main.113/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
70. [The Trickle-down Impact of Reward (In-)consistency on RLHF](https://arxiv.org/abs/2309.16155) *— 2023 · arXiv:2309.16155*
71. [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)*
72. [Training NLI Models Through Universal Adversarial Attack](https://aclanthology.org/2023.ccl-1.72/) *— 2023 · Proceedings of the 22nd Chinese National Conference on Computational Linguistics*
73. [Transformers and the Representation of Biomedical Background Knowledge](https://aclanthology.org/2023.cl-1.2/) *— 2023 · Computational Linguistics*
74. [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)*
75. [Universal and Transferable Adversarial Attacks on Aligned Language Models](https://arxiv.org/abs/2307.15043) *— 2023 · arXiv:2307.15043*
76. [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)*
77. [When and Why Does Bias Mitigation Work?](https://aclanthology.org/2023.findings-emnlp.619/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
78. [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)*
79. [XMD: An End-to-End Framework for Interactive Explanation-Based Debugging of NLP Models](https://aclanthology.org/2023.acl-demo.25/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)*
80. [“World Knowledge” in Multiple Choice Reading Comprehension](https://aclanthology.org/2023.fever-1.5/) *— 2023 · Proceedings of the Sixth Fact Extraction and VERification Workshop (FEVER)*
81. [基于推理链的多跳问答对抗攻击和对抗增强训练方法(Reasoning Chain Based Adversarial Attack and Adversarial Augmentation Training for Multi-hop Question Answering)](https://aclanthology.org/2023.ccl-1.1/) *— 2023 · Proceedings of the 22nd Chinese National Conference on Computational Linguistics*
82. [A Token-level Reference-free Hallucination Detection Benchmark for Free-form Text Generation](https://aclanthology.org/2022.acl-long.464/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
83. [A Word is Worth A Thousand Dollars: Adversarial Attack on Tweets Fools Stock Prediction](https://aclanthology.org/2022.naacl-main.43/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
84. [ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics](https://md.research.elamur.ai/free/en/papers/amrhein-2022-aces-translation-accuracy-challenge-sets/) *— our card · 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
85. [ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation](https://aclanthology.org/2022.emnlp-main.440/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
86. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
87. [ASQA: Factoid Questions Meet Long-Form Answers](https://aclanthology.org/2022.emnlp-main.566/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
88. [Benchmarking the Covariate Shift Robustness of Open-world Intent Classification Approaches](https://aclanthology.org/2022.aacl-short.3/) *— 2022 · Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
89. [Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models?](https://aclanthology.org/2022.coling-1.139/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
90. [Can Explanations Be Useful for Calibrating Black Box Models?](https://aclanthology.org/2022.acl-long.429/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
91. [Can Rationalization Improve Robustness?](https://aclanthology.org/2022.naacl-main.278/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
92. [Challenges in Generalization in Open Domain Question Answering](https://aclanthology.org/2022.findings-naacl.155/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
93. [Claim Extraction and Law Matching for COVID-19-related Legislation](https://aclanthology.org/2022.lrec-1.50/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
94. [CommonsenseQA 2.0: Exposing the Limits of AI through Gamification](https://arxiv.org/abs/2201.05320) *— 2022 · arXiv:2201.05320*
95. [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*
96. [Data-Efficient Concept Extraction from Pre-trained Language Models for Commonsense Explanation Generation](https://aclanthology.org/2022.findings-emnlp.433/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
97. [Data-driven Model Generalizability in Crosslinguistic Low-resource Morphological Segmentation](https://aclanthology.org/2022.tacl-1.23/) *— 2022 · Transactions of the Association for Computational Linguistics*
98. [DataLab: A Platform for Data Analysis and Intervention](https://aclanthology.org/2022.acl-demo.18/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
99. [Debiasing Masks: A New Framework for Shortcut Mitigation in NLU](https://aclanthology.org/2022.emnlp-main.517/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
100. [Deep Learning for Text Style Transfer: A Survey](https://aclanthology.org/2022.cl-1.6/) *— 2022 · Computational Linguistics*

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