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# Who cites «DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over Paragraphs» · page 3

600 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/dua-2019-drop-reading-comprehension-benchmark-requiring/)

Pages: [1](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring/) · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-2/) · **3** · [4](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-4/) · [5](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-5/) · [6](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/dua-2019-drop-reading-comprehension-benchmark-requiring-6/)

1. [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)*
2. [MAF: Multi-Aspect Feedback for Improving Reasoning in Large Language Models](https://aclanthology.org/2023.emnlp-main.407/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
3. [MAFiD: Moving Average Equipped Fusion-in-Decoder for Question Answering over Tabular and Textual Data](https://aclanthology.org/2023.findings-eacl.177/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
4. [Making Language Models Better Reasoners with Step-Aware Verifier](https://aclanthology.org/2023.acl-long.291/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
5. [MarkQA: A large scale KBQA dataset with numerical reasoning](https://aclanthology.org/2023.emnlp-main.633/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
6. [MatCha: Enhancing Visual Language Pretraining with Math Reasoning and Chart Derendering](https://aclanthology.org/2023.acl-long.714/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models](https://arxiv.org/abs/2309.12284) *— 2023 · arXiv:2309.12284*
8. [MoT: Memory-of-Thought Enables ChatGPT to Self-Improve](https://aclanthology.org/2023.emnlp-main.392/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
9. [Multi-Row, Multi-Span Distant Supervision For Table+Text Question Answering](https://aclanthology.org/2023.acl-long.449/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
10. [Navigate through Enigmatic Labyrinth A Survey of Chain of Thought Reasoning: Advances, Frontiers and Future](https://arxiv.org/abs/2309.15402) *— 2023 · arXiv:2309.15402*
11. [Numeric Magnitude Comparison Effects in Large Language Models](https://aclanthology.org/2023.findings-acl.383/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
12. [Orca 2: Teaching Small Language Models How to Reason](https://arxiv.org/abs/2311.11045) *— 2023 · arXiv:2311.11045*
13. [PaLM 2 Technical Report](https://arxiv.org/abs/2305.10403) *— 2023 · arXiv:2305.10403*
14. [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)*
15. [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*
16. [RA-DIT: Retrieval-Augmented Dual Instruction Tuning](https://arxiv.org/abs/2310.01352) *— 2023 · arXiv:2310.01352*
17. [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*
18. [ReCEval: Evaluating Reasoning Chains via Correctness and Informativeness](https://aclanthology.org/2023.emnlp-main.622/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
19. [Reasoning over Public and Private Data in Retrieval-Based Systems](https://aclanthology.org/2023.tacl-1.51/) *— 2023 · Transactions of the Association for Computational Linguistics*
20. [Robust Prompt Optimization for Large Language Models Against Distribution Shifts](https://aclanthology.org/2023.emnlp-main.95/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
21. [SEER : A Knapsack approach to Exemplar Selection for In-Context HybridQA](https://aclanthology.org/2023.emnlp-main.837/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
22. [SlideVQA: A Dataset for Document Visual Question Answering on Multiple Images](https://arxiv.org/abs/2301.04883) *— 2023 · arXiv:2301.04883*
23. [Tab-CQA: A Tabular Conversational Question Answering Dataset on Financial Reports](https://aclanthology.org/2023.acl-industry.20/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 5: Industry Track)*
24. [Unifying Structure Reasoning and Language Model Pre-training for Complex Reasoning](https://arxiv.org/abs/2301.08913) *— 2023 · arXiv:2301.08913*
25. [VILA: On Pre-training for Visual Language Models](https://arxiv.org/abs/2312.07533) *— 2023 · arXiv:2312.07533*
26. [Weakly-Supervised Questions for Zero-Shot Relation Extraction](https://aclanthology.org/2023.eacl-main.224/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
27. [When Do Decompositions Help for Machine Reading?](https://aclanthology.org/2023.emnlp-main.219/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
28. [ZO-AdaMU Optimizer: Adapting Perturbation by the Momentum and Uncertainty in Zeroth-order Optimization](https://arxiv.org/abs/2312.15184) *— 2023 · arXiv:2312.15184*
29. [``John is 50 years old, can his son be 65?'''' Evaluating NLP Models'' Understanding of Feasibility](https://aclanthology.org/2023.eacl-main.30/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
30. [A Few More Examples May Be Worth Billions of Parameters](https://aclanthology.org/2022.findings-emnlp.72/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
31. [ANLIzing the Adversarial Natural Language Inference Dataset](https://aclanthology.org/2022.scil-1.3/) *— 2022 · Proceedings of the Society for Computation in Linguistics 2022*
32. [AdaLoGN: Adaptive Logic Graph Network for Reasoning-Based Machine Reading Comprehension](https://aclanthology.org/2022.acl-long.494/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
33. [Addressing Resource and Privacy Constraints in Semantic Parsing Through Data Augmentation](https://aclanthology.org/2022.findings-acl.291/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
34. [An MRC Framework for Semantic Role Labeling](https://aclanthology.org/2022.coling-1.191/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
35. [Analytical Reasoning of Text](https://aclanthology.org/2022.findings-naacl.177/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
36. [Analyzing Dynamic Adversarial Training Data in the Limit](https://aclanthology.org/2022.findings-acl.18/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
37. [Analyzing Multi-Task Learning for Abstractive Text Summarization](https://aclanthology.org/2022.gem-1.5/) *— 2022 · Proceedings of the Second Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)*
38. [Answering Numerical Reasoning Questions in Table-Text Hybrid Contents with Graph-based Encoder and Tree-based Decoder](https://aclanthology.org/2022.coling-1.118/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
39. [Ask Me Anything: A simple strategy for prompting language models](https://arxiv.org/abs/2210.02441) *— 2022 · arXiv:2210.02441*
40. [BanglaRQA: A Benchmark Dataset for Under-resourced Bangla Language Reading Comprehension-based Question Answering with Diverse Question-Answer Types](https://aclanthology.org/2022.findings-emnlp.186/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
41. [Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models](https://arxiv.org/abs/2206.04615) *— 2022 · arXiv:2206.04615 · Transactions on Machine Learning Research, May/2022, https://openreview.net/forum?id=uyTL5Bvosj*
42. [ByT5: Towards a Token-Free Future with Pre-trained Byte-to-Byte Models](https://aclanthology.org/2022.tacl-1.17/) *— 2022*
43. [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*
44. [Case-Based Abductive Natural Language Inference](https://aclanthology.org/2022.coling-1.134/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
45. [Chaining Simultaneous Thoughts for Numerical Reasoning](https://aclanthology.org/2022.findings-emnlp.187/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
46. [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*
47. [CommonsenseQA 2.0: Exposing the Limits of AI through Gamification](https://arxiv.org/abs/2201.05320) *— 2022 · arXiv:2201.05320*
48. [Complex Reading Comprehension Through Question Decomposition](https://aclanthology.org/2022.alta-1.5/) *— 2022 · Proceedings of the 20th Annual Workshop of the Australasian Language Technology Association*
49. [ConvFinQA: Exploring the Chain of Numerical Reasoning in Conversational Finance Question Answering](https://aclanthology.org/2022.emnlp-main.421/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
50. [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)*
51. [Curriculum: A Broad-Coverage Benchmark for Linguistic Phenomena in Natural Language Understanding](https://aclanthology.org/2022.naacl-main.234/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
52. [Domain Adaptation for Question Answering via Question Classification](https://aclanthology.org/2022.coling-1.153/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
53. [Efficient Training of Language Models to Fill in the Middle](https://arxiv.org/abs/2207.14255) *— 2022 · arXiv:2207.14255*
54. [Efficiently Tuned Parameters Are Task Embeddings](https://aclanthology.org/2022.emnlp-main.334/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
55. [Enhancing Financial Table and Text Question Answering with Tabular Graph and Numerical Reasoning](https://aclanthology.org/2022.aacl-main.72/) *— 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 1: Long Papers)*
56. [Exploiting Numerical-Contextual Knowledge to Improve Numerical Reasoning in Question Answering](https://aclanthology.org/2022.findings-naacl.138/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
57. [Exploring The Landscape of Distributional Robustness for Question Answering Models](https://aclanthology.org/2022.findings-emnlp.441/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
58. [FORTAP: Using Formulas for Numerical-Reasoning-Aware Table Pretraining](https://aclanthology.org/2022.acl-long.82/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
59. [FQuAD2.0: French Question Answering and Learning When You Don''t Know](https://aclanthology.org/2022.lrec-1.237/) *— 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference*
60. [Generalized Quantifiers as a Source of Error in Multilingual NLU Benchmarks](https://aclanthology.org/2022.naacl-main.359/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
61. [Hey AI, Can You Solve Complex Tasks by Talking to Agents?](https://aclanthology.org/2022.findings-acl.142/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
62. [Hyperdecoders: Instance-specific decoders for multi-task NLP](https://aclanthology.org/2022.findings-emnlp.124/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
63. [Identifying Weaknesses in Machine Translation Metrics Through Minimum Bayes Risk Decoding: A Case Study for COMET](https://aclanthology.org/2022.aacl-main.83/) *— 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 1: Long Papers)*
64. [Impact of Pretraining Term Frequencies on Few-Shot Numerical Reasoning](https://aclanthology.org/2022.findings-emnlp.59/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
65. [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*
66. [Interpretable Proof Generation via Iterative Backward Reasoning](https://aclanthology.org/2022.naacl-main.216/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
67. [Is a Question Decomposition Unit All We Need?](https://aclanthology.org/2022.emnlp-main.302/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
68. [LILA: A Unified Benchmark for Mathematical Reasoning](https://aclanthology.org/2022.emnlp-main.392/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
69. [Learning to Imagine: Integrating Counterfactual Thinking in Neural Discrete Reasoning](https://aclanthology.org/2022.acl-long.5/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
70. [Least-to-most prompting enables complex reasoning in large language models](https://arxiv.org/abs/2205.10625) *— 2022*
71. [MWP-BERT: Numeracy-Augmented Pre-training for Math Word Problem Solving](https://aclanthology.org/2022.findings-naacl.74/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
72. [Measurement Extraction with Natural Language Processing: A Review](https://aclanthology.org/2022.findings-emnlp.161/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
73. [Mintaka: A Complex, Natural, and Multilingual Dataset for End-to-End Question Answering](https://aclanthology.org/2022.coling-1.138/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
74. [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*
75. [Modeling Multi-hop Question Answering as Single Sequence Prediction](https://aclanthology.org/2022.acl-long.69/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
76. [Models in the Loop: Aiding Crowdworkers with Generative Annotation Assistants](https://aclanthology.org/2022.naacl-main.275/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
77. [MultiHiertt: Numerical Reasoning over Multi Hierarchical Tabular and Textual Data](https://aclanthology.org/2022.acl-long.454/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
78. [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*
79. [OPERA: Operation-Pivoted Discrete Reasoning over Text](https://aclanthology.org/2022.naacl-main.119/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
80. [On the Limitations of Dataset Balancing: The Lost Battle Against Spurious Correlations](https://aclanthology.org/2022.findings-naacl.168/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
81. [PACIFIC: Towards Proactive Conversational Question Answering over Tabular and Textual Data in Finance](https://aclanthology.org/2022.emnlp-main.469/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
82. [PaLM: Scaling Language Modeling with Pathways](https://arxiv.org/abs/2204.02311) *— 2022 · arXiv:2204.02311*
83. [Perturbation Augmentation for Fairer NLP](https://aclanthology.org/2022.emnlp-main.646/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
84. [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*
85. [Program of Thoughts Prompting: Disentangling Computation from Reasoning for Numerical Reasoning Tasks](https://arxiv.org/abs/2211.12588) *— 2022 · arXiv:2211.12588*
86. [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*
87. [Question Answering Infused Pre-training of General-Purpose Contextualized Representations](https://aclanthology.org/2022.findings-acl.59/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
88. [ROSCOE: A Suite of Metrics for Scoring Step-by-Step Reasoning](https://arxiv.org/abs/2212.07919) *— 2022 · arXiv:2212.07919*
89. [Reason first, then respond: Modular Generation for Knowledge-infused Dialogue](https://aclanthology.org/2022.findings-emnlp.527/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
90. [Reasoning Like Program Executors](https://aclanthology.org/2022.emnlp-main.48/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
91. [Reasoning with Language Model Prompting: A Survey](https://arxiv.org/abs/2212.09597) *— 2022 · arXiv:2212.09597*
92. [Reframing Instructional Prompts to GPTk's Language](https://aclanthology.org/2022.findings-acl.50/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
93. [SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://aclanthology.org/2022.acl-long.346/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
94. [Successive Prompting for Decomposing Complex Questions](https://aclanthology.org/2022.emnlp-main.81/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
95. [TaCube: Pre-computing Data Cubes for Answering Numerical-Reasoning Questions over Tabular Data](https://aclanthology.org/2022.emnlp-main.145/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
96. [Teaching Broad Reasoning Skills for Multi-Step QA by Generating Hard Contexts](https://aclanthology.org/2022.emnlp-main.439/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
97. [To What Extent Do Natural Language Understanding Datasets Correlate to Logical Reasoning? A Method for Diagnosing Logical Reasoning.](https://aclanthology.org/2022.coling-1.147/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
98. [Towards Robust Numerical Question Answering: Diagnosing Numerical Capabilities of NLP Systems](https://aclanthology.org/2022.emnlp-main.542/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
99. [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)*
100. [Training language models to follow instructions with human feedback](https://arxiv.org/abs/2203.02155) *— 2022 · arXiv:2203.02155*

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