---
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/"
---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Adversarial Examples for Evaluating Reading…](https://md.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/) / cited by

# 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.research.elamur.ai/free/en/papers/jia-2017-adversarial-examples-evaluating-reading-comprehension/)

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

1. [A Dual-Task Paradigm to Investigate Sentence Comprehension Strategies in Language Models](https://aclanthology.org/2026.acl-long.552/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [AI, Take the Wheel: What Drives Delegation and Trust in Human–Computer Cooperative Question Answering?](https://aclanthology.org/2026.findings-acl.422/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
3. [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*
4. [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*
5. [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)*
6. [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*
7. [From Factuality to Meta-Factivity: A Cognitive Blueprint for Trustworthy LLMs](https://aclanthology.org/2026.acl-short.7/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
8. [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*
9. [Immediate Inference: The Missing Foundation in Large Language Model Logical Reasoning](https://aclanthology.org/2026.acl-long.808/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [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)*
11. [Modeling Human Adversarial Strategy Adaptation in Multi-Turn Language Model Interactions](https://aclanthology.org/2026.conll-main.22/) *— 2026 · Proceedings of the 30th Conference on Computational Natural Language Learning*
12. [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*
13. [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*
14. [Parameter-Efficient Quantum NLP for Paraphrase Detection: Performance, Robustness, and Entanglement](https://arxiv.org/abs/2609.14529) *— 2026 · arXiv:2609.14529*
15. [Rethinking RL Evaluation: Can Benchmarks Truly Reveal Failures of RL Methods?](https://aclanthology.org/2026.findings-acl.769/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
16. [StanceAttack: Adversarial Attack for Stance Detection](https://aclanthology.org/2026.findings-acl.2034/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
17. [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*
18. [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*
19. [A Template Is All You Meme](https://aclanthology.org/2025.naacl-long.525/) *— 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)*
20. [Adversarial Defense without Adversarial Defense: Enhancing Language Model Robustness via Instance-level Principal Component Removal](https://aclanthology.org/2025.tacl-1.63/) *— 2025 · Transactions of the Association for Computational Linguistics*
21. [Cancer-Myth: Evaluating Large Language Models on Patient Questions with False Presuppositions](https://arxiv.org/abs/2504.11373) *— 2025 · arXiv:2504.11373*
22. [Corpus Poisoning via Approximate Greedy Gradient Descent](https://aclanthology.org/2025.findings-acl.222/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
23. [Curriculum Debiasing: Toward Robust Parameter-Efficient Fine-Tuning Against Dataset Biases](https://aclanthology.org/2025.acl-long.469/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [DARE: Diverse Visual Question Answering with Robustness Evaluation](https://aclanthology.org/2025.tacl-1.52/) *— 2025 · Transactions of the Association for Computational Linguistics*
25. [HellaSwag-Pro: A Large-Scale Bilingual Benchmark for Evaluating the Robustness of LLMs in Commonsense Reasoning](https://aclanthology.org/2025.findings-acl.472/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
26. [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)*
27. [KRISTEVA: Close Reading as a Novel Task for Benchmarking Interpretive Reasoning](https://aclanthology.org/2025.acl-long.1577/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [LLM-based Adversarial Dataset Augmentation for Automatic Media Bias Detection](https://aclanthology.org/2025.latechclfl-1.3/) *— 2025 · Proceedings of the 9th Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature (LaTeCH-CLfL 2025)*
29. [MALAMUTE: A Multilingual, Highly-granular, Template-free, Education-based Probing Dataset](https://aclanthology.org/2025.findings-acl.209/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
30. [Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets](https://aclanthology.org/2025.cl-1.4/) *— 2025 · Computational Linguistics*
31. [Minimizing Queries, Maximizing Impact: Adaptive Score-Based Attack and Defense for Sentiment Analysis](https://aclanthology.org/2025.ijcnlp-long.15/) *— 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*
32. [Mitigating Shortcut Learning with InterpoLated Learning](https://aclanthology.org/2025.acl-long.450/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
33. [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*
34. [ParetoRAG: Leveraging Sentence-Context Attention for Robust and Efficient Retrieval-Augmented Generation](https://aclanthology.org/2025.findings-emnlp.220/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
35. [People will agree what I think: Investigating LLM”s False Consensus Effect](https://aclanthology.org/2025.findings-naacl.6/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
36. [QAVA: Query-Agnostic Visual Attack to Large Vision-Language Models](https://aclanthology.org/2025.naacl-long.512/) *— 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)*
37. [Ranking Over Scoring: Towards Reliable and Robust Automated Evaluation of LLM-Generated Medical Explanatory Arguments](https://aclanthology.org/2025.coling-main.634/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
38. [Robustness Evaluation of the German Extractive Question Answering Task](https://aclanthology.org/2025.coling-main.121/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
39. [SPIRIT: Patching Speech Language Models against Jailbreak Attacks](https://aclanthology.org/2025.emnlp-main.734/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
40. [Spurious Correlations and Beyond: Understanding and Mitigating Shortcut Learning in SDOH Extraction with Large Language Models](https://aclanthology.org/2025.acl-short.86/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
41. [TaeBench: Improving Quality of Toxic Adversarial Examples](https://aclanthology.org/2025.naacl-industry.21/) *— 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)*
42. [TrapDoc: Deceiving LLM Users by Injecting Imperceptible Phantom Tokens into Documents](https://aclanthology.org/2025.findings-emnlp.1027/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
43. [Typology-Guided Adaptation in Multilingual Models](https://aclanthology.org/2025.acl-long.1059/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
44. [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)*
45. [A Comprehensive Survey of Contamination Detection Methods in Large Language Models](https://arxiv.org/abs/2404.00699) *— 2024 · arXiv:2404.00699*
46. [A Curious Case of Searching for the Correlation between Training Data and Adversarial Robustness of Transformer Textual Models](https://aclanthology.org/2024.findings-acl.800/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
47. [Adversarial Math Word Problem Generation](https://aclanthology.org/2024.findings-emnlp.292/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
48. [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)*
49. [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)*
50. [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*
51. [Context-aware Adversarial Attack on Named Entity Recognition](https://aclanthology.org/2024.wnut-1.2/) *— 2024 · Proceedings of the Ninth Workshop on Noisy and User-generated Text (W-NUT 2024)*
52. [Cross-Lingual Transfer Robustness to Lower-Resource Languages on Adversarial Datasets](https://aclanthology.org/2024.lrec-main.372/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
53. [Cutting Through the Noise: Boosting LLM Performance on Math Word Problems](https://md.research.elamur.ai/free/en/papers/anantheswaran-2024-cutting-through-noise-boosting-llm/) *— our card · 2024 · arXiv:2406.15444*
54. [DA$^3$: A Distribution-Aware Adversarial Attack against Language Models](https://aclanthology.org/2024.emnlp-main.107/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
55. [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)*
56. [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*
57. [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*
58. [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)*
59. [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)*
60. [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)*
61. [Extreme Miscalibration and the Illusion of Adversarial Robustness](https://aclanthology.org/2024.acl-long.137/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [Fast Adversarial Attacks on Language Models In One GPU Minute](https://arxiv.org/abs/2402.15570) *— 2024 · arXiv:2402.15570*
63. [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)*
64. [First Heuristic Then Rational: Dynamic Use of Heuristics in Language Model Reasoning](https://aclanthology.org/2024.emnlp-main.789/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
65. [From Shortcuts to Triggers: Backdoor Defense with Denoised PoE](https://aclanthology.org/2024.naacl-long.27/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
66. [InfFeed: Influence Functions as a Feedback to Improve the Performance of Subjective Tasks](https://aclanthology.org/2024.lrec-main.794/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
67. [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)*
68. [Investigating the Generalizability of Pretrained Language Models across Multiple Dimensions: A Case Study of NLI and MRC](https://aclanthology.org/2024.genbench-1.11/) *— 2024 · Proceedings of the 2nd GenBench Workshop on Generalisation (Benchmarking) in NLP*
69. [It Is Not About What You Say, It Is About How You Say It: A Surprisingly Simple Approach for Improving Reading Comprehension](https://aclanthology.org/2024.findings-acl.491/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
70. [KU-DMIS at EHRSQL 2024 : Generating SQL query via question templatization in EHR](https://aclanthology.org/2024.clinicalnlp-1.64/) *— 2024 · Proceedings of the 6th Clinical Natural Language Processing Workshop*
71. [Machine Translation Testing via Syntactic Tree Pruning](https://arxiv.org/abs/2401.00751) *— 2024 · arXiv:2401.00751*
72. [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*
73. [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*
74. [On the Relationship between Skill Neurons and Robustness in Prompt Tuning](https://aclanthology.org/2024.lrec-main.1086/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
75. [Optimal and efficient text counterfactuals using Graph Neural Networks](https://aclanthology.org/2024.blackboxnlp-1.1/) *— 2024 · Proceedings of the 7th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP*
76. [Pointing Out the Shortcomings of Relation Extraction Models with Semantically Motivated Adversarials](https://aclanthology.org/2024.lrec-main.1121/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
77. [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*
78. [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*
79. [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*
80. [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*
81. [Reinforcement Learning-Driven LLM Agent for Automated Attacks on LLMs](https://aclanthology.org/2024.privatenlp-1.17/) *— 2024 · Proceedings of the Fifth Workshop on Privacy in Natural Language Processing*
82. [Rethinking Word-level Adversarial Attack: The Trade-off between Efficiency, Effectiveness, and Imperceptibility](https://aclanthology.org/2024.lrec-main.1223/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
83. [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)*
84. [Simpler Becomes Harder: Do LLMs Exhibit a Coherent Behavior on Simplified Corpora?](https://aclanthology.org/2024.determit-1.17/) *— 2024 · Proceedings of the Workshop on DeTermIt! Evaluating Text Difficulty in a Multilingual Context @ LREC-COLING 2024*
85. [TEMA: Token Embeddings Mapping for Enriching Low-Resource Language Models](https://aclanthology.org/2024.emnlp-main.638/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
86. [The Art of Saying No: Contextual Noncompliance in Language Models](https://arxiv.org/abs/2407.12043) *— 2024 · arXiv:2407.12043*
87. [The Earth is Flat because...: Investigating LLMs” Belief towards Misinformation via Persuasive Conversation](https://aclanthology.org/2024.acl-long.858/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [The Llama 3 Herd of Models](https://arxiv.org/abs/2407.21783) *— 2024 · arXiv:2407.21783*
89. [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)*
90. [Toward Robust RALMs: Revealing the Impact of Imperfect Retrieval on Retrieval-Augmented Language Models](https://aclanthology.org/2024.tacl-1.91/) *— 2024 · Transactions of the Association for Computational Linguistics*
91. [Towards Robust Extractive Question Answering Models: Rethinking the Training Methodology](https://aclanthology.org/2024.findings-emnlp.121/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
92. [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)*
93. [Unveiling Narrative Reasoning Limits of Large Language Models with Trope in Movie Synopses](https://aclanthology.org/2024.findings-emnlp.872/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
94. [VISREAS: Complex Visual Reasoning with Unanswerable Questions](https://aclanthology.org/2024.findings-acl.402/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
95. [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)*
96. [What Makes Language Models Good-enough?](https://aclanthology.org/2024.findings-acl.913/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
97. [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)*
98. [XAI-Attack: Utilizing Explainable AI to Find Incorrectly Learned Patterns for Black-Box Adversarial Example Creation](https://aclanthology.org/2024.lrec-main.1542/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
99. [“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*
100. [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*

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