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# Who cites «Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning»

109 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/dasigi-2019-quoref-reading-comprehension-dataset-questions/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dasigi-2019-quoref-reading-comprehension-dataset-questions-2/)

1. [The Validity of Coreference-based Evaluations of Natural Language Understanding](https://arxiv.org/abs/2602.16200) *— 2026 · arXiv:2602.16200*
2. [Verify Before You Commit: Towards Faithful Reasoning in LLM Agents via Self-Auditing](https://aclanthology.org/2026.acl-long.1440/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
3. [AQuilt: Weaving Logic and Self-Inspection into Low-Cost, High-Relevance Data Synthesis for Specialist LLMs](https://aclanthology.org/2025.emnlp-main.293/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
4. [From Ambiguity to Accuracy: The Transformative Effect of Coreference Resolution on Retrieval-Augmented Generation systems](https://aclanthology.org/2025.acl-srw.27/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
5. [SimRAG: Self-Improving Retrieval-Augmented Generation for Adapting Large Language Models to Specialized Domains](https://aclanthology.org/2025.naacl-long.575/) *— 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)*
6. [Unlocking Speech Instruction Data Potential with Query Rewriting](https://aclanthology.org/2025.findings-acl.534/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
7. [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://arxiv.org/abs/2402.07827) *— 2024 · arXiv:2402.07827*
8. [Challenges to Evaluating the Generalization of Coreference Resolution Models: A Measurement Modeling Perspective](https://md.mova.elamur.ai/free/en/docs/papers/porada-2024-challenges-evaluating-generalization-coreference-resolution/) *— our card · 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
9. [Continual Learning of Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2404.16789) *— 2024 · arXiv:2404.16789*
10. [Enhancing Pre-Trained Generative Language Models with Question Attended Span Extraction on Machine Reading Comprehension](https://aclanthology.org/2024.emnlp-main.560/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
11. [Maverick: Efficient and Accurate Coreference Resolution Defying Recent Trends](https://aclanthology.org/2024.acl-long.722/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
12. [Multi-Task Inference: Can Large Language Models Follow Multiple Instructions at Once?](https://aclanthology.org/2024.acl-long.304/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
13. [Multi-Task Learning with Adapters for Plausibility Prediction: Bridging the Gap or Falling into the Trenches?](https://aclanthology.org/2024.insights-1.18/) *— 2024 · Proceedings of the Fifth Workshop on Insights from Negative Results in NLP*
14. [Paraphrasing in Affirmative Terms Improves Negation Understanding](https://aclanthology.org/2024.acl-short.55/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
15. [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)*
16. [Analyzing the Effectiveness of the Underlying Reasoning Tasks in Multi-hop Question Answering](https://aclanthology.org/2023.findings-eacl.87/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
17. [Best-k Search Algorithm for Neural Text Generation](https://aclanthology.org/2023.acl-long.692/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [Complex Reasoning in Natural Language](https://aclanthology.org/2023.acl-tutorials.2/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 6: Tutorial Abstracts)*
19. [Domain Incremental Lifelong Learning in an Open World](https://aclanthology.org/2023.findings-acl.361/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
20. [Don''t Blame the Annotator: Bias Already Starts in the Annotation Instructions](https://aclanthology.org/2023.eacl-main.130/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
21. [Dual Cache for Long Document Neural Coreference Resolution](https://aclanthology.org/2023.acl-long.851/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
22. [Ellipsis-Dependent Reasoning: a New Challenge for Large Language Models](https://aclanthology.org/2023.acl-short.4/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
23. [FERMAT: An Alternative to Accuracy for Numerical Reasoning](https://aclanthology.org/2023.acl-long.838/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [Few-shot Adaptation Works with UnpredicTable Data](https://aclanthology.org/2023.acl-long.102/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
25. [FiD-ICL: A Fusion-in-Decoder Approach for Efficient In-Context Learning](https://aclanthology.org/2023.acl-long.454/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [How Many Answers Should I Give? An Empirical Study of Multi-Answer Reading Comprehension](https://aclanthology.org/2023.findings-acl.359/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
27. [Instructive Dialogue Summarization with Query Aggregations](https://aclanthology.org/2023.emnlp-main.474/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
28. [Learning to Initialize: Can Meta Learning Improve Cross-task Generalization in Prompt Tuning?](https://aclanthology.org/2023.acl-long.659/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
29. [Long-Tailed Question Answering in an Open World](https://aclanthology.org/2023.acl-long.351/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [Pre-Training to Learn in Context](https://aclanthology.org/2023.acl-long.267/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
31. [Prompting with Pseudo-Code Instructions](https://aclanthology.org/2023.emnlp-main.939/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
32. [RQUGE: Reference-Free Metric for Evaluating Question Generation by Answering the Question](https://aclanthology.org/2023.findings-acl.428/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
33. [ZEROTOP: Zero-Shot Task-Oriented Semantic Parsing using Large Language Models](https://aclanthology.org/2023.emnlp-main.354/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
34. [CMQA: A Dataset of Conditional Question Answering with Multiple-Span Answers](https://aclanthology.org/2022.coling-1.146/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
35. [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*
36. [CommonsenseQA 2.0: Exposing the Limits of AI through Gamification](https://arxiv.org/abs/2201.05320) *— 2022 · arXiv:2201.05320*
37. [ConTinTin: Continual Learning from Task Instructions](https://aclanthology.org/2022.acl-long.218/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
38. [Cross-Task Generalization via Natural Language Crowdsourcing Instructions](https://aclanthology.org/2022.acl-long.244/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
39. [Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts](https://aclanthology.org/2022.findings-emnlp.189/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
40. [F-coref: Fast, Accurate and Easy to Use Coreference Resolution](https://aclanthology.org/2022.aacl-demo.6/) *— 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: System Demonstrations*
41. [Galactica: A Large Language Model for Science](https://arxiv.org/abs/2211.09085) *— 2022 · arXiv:2211.09085*
42. [Improving In-Context Few-Shot Learning via Self-Supervised Training](https://aclanthology.org/2022.naacl-main.260/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
43. [Learning Instructions with Unlabeled Data for Zero-Shot Cross-Task Generalization](https://aclanthology.org/2022.emnlp-main.105/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
44. [Machine Reading, Fast and Slow: When Do Models ``Understand'''' Language?](https://aclanthology.org/2022.coling-1.8/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
45. [MixQG: Neural Question Generation with Mixed Answer Types](https://aclanthology.org/2022.findings-naacl.111/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
46. [MultiSpanQA: A Dataset for Multi-Span Question Answering](https://aclanthology.org/2022.naacl-main.90/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
47. [On Measuring Social Biases in Prompt-Based Multi-Task Learning](https://aclanthology.org/2022.findings-naacl.42/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
48. [Polyglot Prompt: Multilingual Multitask Prompt Training](https://aclanthology.org/2022.emnlp-main.674/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
49. [ProQA: Structural Prompt-based Pre-training for Unified Question Answering](https://aclanthology.org/2022.naacl-main.313/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
50. [Prompt Tuning for Discriminative Pre-trained Language Models](https://aclanthology.org/2022.findings-acl.273/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
51. [Reasoning Like Program Executors](https://aclanthology.org/2022.emnlp-main.48/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
52. [Reframing Instructional Prompts to GPTk's Language](https://aclanthology.org/2022.findings-acl.50/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
53. [Retrieval-guided Counterfactual Generation for QA](https://aclanthology.org/2022.acl-long.117/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
54. [Tracing Origins: Coreference-aware Machine Reading Comprehension](https://aclanthology.org/2022.acl-long.91/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
55. [UKP-SQUARE: An Online Platform for Question Answering Research](https://aclanthology.org/2022.acl-demo.2/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
56. [A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers](https://aclanthology.org/2021.naacl-main.365/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
57. [Benchmarking Machine Reading Comprehension: A Psychological Perspective](https://aclanthology.org/2021.eacl-main.137/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*
58. [Comparing Test Sets with Item Response Theory](https://aclanthology.org/2021.acl-long.92/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
59. [Coreference Reasoning in Machine Reading Comprehension](https://aclanthology.org/2021.acl-long.448/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
60. [Coreference-Aware Dialogue Summarization](https://aclanthology.org/2021.sigdial-1.53/) *— 2021 · Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue*
61. [CrossFit: A Few-shot Learning Challenge for Cross-task Generalization in NLP](https://arxiv.org/abs/2104.08835) *— 2021 · arXiv:2104.08835*
62. [Evaluation Paradigms in Question Answering](https://aclanthology.org/2021.emnlp-main.758/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
63. [Few-Shot Question Answering by Pretraining Span Selection](https://aclanthology.org/2021.acl-long.239/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
64. [How Can We Know When Language Models Know? On the Calibration of Language Models for Question Answering](https://aclanthology.org/2021.tacl-1.57/) *— 2021*
65. [Improving the Robustness of QA Models to Challenge Sets with Variational Question-Answer Pair Generation](https://aclanthology.org/2021.acl-srw.21/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: Student Research Workshop*
66. [Incorporating Syntax and Semantics in Coreference Resolution with Heterogeneous Graph Attention Network](https://aclanthology.org/2021.naacl-main.125/) *— 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
67. [Learning with Instance Bundles for Reading Comprehension](https://aclanthology.org/2021.emnlp-main.584/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
68. [MetaICL: Learning to Learn In Context](https://arxiv.org/abs/2110.15943) *— 2021 · arXiv:2110.15943*
69. [Multitask Prompted Training Enables Zero-Shot Task Generalization](https://arxiv.org/abs/2110.08207) *— 2021 · arXiv:2110.08207*
70. [On the Efficacy of Adversarial Data Collection for Question Answering: Results from a Large-Scale Randomized Study](https://aclanthology.org/2021.acl-long.517/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)*
71. [SPARTQA: A Textual Question Answering Benchmark for Spatial Reasoning](https://md.mova.elamur.ai/free/en/docs/papers/mirzaee-2021-spartqa-textual-question-answering-benchmark/) *— our card · 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
72. [Understanding Mention Detector-Linker Interaction in Neural Coreference Resolution](https://aclanthology.org/2021.crac-1.16/) *— 2021 · Proceedings of the Fourth Workshop on Computational Models of Reference, Anaphora and Coreference*
73. [Unsupervised Pronoun Resolution via Masked Noun-Phrase Prediction](https://aclanthology.org/2021.acl-short.117/) *— 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 2: Short Papers)*
74. [What to Pre-Train on? Efficient Intermediate Task Selection](https://aclanthology.org/2021.emnlp-main.827/) *— 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing*
75. [Why Machine Reading Comprehension Models Learn Shortcuts?](https://aclanthology.org/2021.findings-acl.85/) *— 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021*
76. [A Knowledge-Driven Approach to Classifying Object and Attribute Coreferences in Opinion Mining](https://aclanthology.org/2020.findings-emnlp.146/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
77. [A Simple and Effective Model for Answering Multi-span Questions](https://aclanthology.org/2020.emnlp-main.248/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
78. [Active Learning for Coreference Resolution using Discrete Annotation](https://aclanthology.org/2020.acl-main.738/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
79. [Beat the AI: Investigating Adversarial Human Annotation for Reading Comprehension](https://aclanthology.org/2020.tacl-1.43/) *— 2020*
80. [Benefits of Intermediate Annotations in Reading Comprehension](https://aclanthology.org/2020.acl-main.497/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
81. [CorefQA: Coreference Resolution as Query-based Span Prediction](https://aclanthology.org/2020.acl-main.622/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
82. [Coreferential Reasoning Learning for Language Representation](https://aclanthology.org/2020.emnlp-main.582/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
83. [Dynamic Sampling Strategies for Multi-Task Reading Comprehension](https://aclanthology.org/2020.acl-main.86/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
84. [Evaluating Models' Local Decision Boundaries via Contrast Sets](https://md.mova.elamur.ai/free/en/docs/papers/gardner-2020-evaluating-models-local-decision-boundaries/) *— our card · 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
85. [IIRC: A Dataset of Incomplete Information Reading Comprehension Questions](https://aclanthology.org/2020.emnlp-main.86/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
86. [Incremental Neural Coreference Resolution in Constant Memory](https://md.mova.elamur.ai/free/en/docs/papers/xia-2020-incremental-neural-coreference-resolution-constant/) *— our card · 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
87. [Learning from Task Descriptions](https://aclanthology.org/2020.emnlp-main.105/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
88. [MOCHA: A Dataset for Training and Evaluating Generative Reading Comprehension Metrics](https://aclanthology.org/2020.emnlp-main.528/) *— 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
89. [Overestimation of Syntactic Representation in Neural Language Models](https://aclanthology.org/2020.acl-main.160/) *— 2020 · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics*
90. [TORQUE: A Reading Comprehension Dataset of Temporal Ordering Questions](https://md.mova.elamur.ai/free/en/docs/papers/ning-2020-torque-reading-comprehension-dataset-temporal/) *— our card · 2020 · Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)*
91. [To Test Machine Comprehension, Start by Defining Comprehension](https://arxiv.org/abs/2005.01525) *— 2020 · arXiv:2005.01525*
92. [UNIFIEDQA: Crossing Format Boundaries with a Single QA System](https://aclanthology.org/2020.findings-emnlp.171/) *— 2020 · Findings of the Association for Computational Linguistics: EMNLP 2020*
93. [Comprehensive Multi-Dataset Evaluation of Reading Comprehension](https://aclanthology.org/D19-5820/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
94. [On Making Reading Comprehension More Comprehensive](https://aclanthology.org/D19-5815/) *— 2019 · Proceedings of the 2nd Workshop on Machine Reading for Question Answering*
95. bansal-2023-few-shot-unified-question-answering
96. bojic-2025-smartminer-extracting-evaluating-smart-goals
97. choudhury-2022-can-edge-probing-tests-reveal
98. elazar-2020-extraordinary-failure-complement-coercion-crowdsourcing
99. li-2025-glider-global-local-instruction-driven
100. lin-2026-full-duplex-bench-v2-multi

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