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

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/)

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1. [Apertus: Democratizing Open and Compliant LLMs for Global Language Environments](https://aclanthology.org/2026.acl-long.2172/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
2. [BayesFlow: A Probability Inference Framework for Meta-Agent Assisted Workflow Generation](https://aclanthology.org/2026.findings-eacl.165/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 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. [CASPER: Bridging Discrete and Continuous Prompt Optimization through Feedback-Guided Gradient Descent](https://aclanthology.org/2026.eacl-industry.32/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 5: Industry Track)*
5. [Conditional Memory via Scalable Lookup: A New Axis of Sparsity for Large Language Models](https://aclanthology.org/2026.acl-long.226/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [CreditDecoding: Accelerating Parallel Decoding in Diffusion Large Language Models with Trace Credit](https://aclanthology.org/2026.acl-long.509/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [DeepSeek-V4.1-Flash: Pushing the Limits of KV Cache Compression](https://arxiv.org/abs/2609.19969) *— 2026 · arXiv:2609.19969*
8. [DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence](https://arxiv.org/abs/2606.19348) *— 2026 · arXiv:2606.19348*
9. [Demystifying Mixed Outcomes of Self-Training: Pre-training Analyses on Non-Toy LLMs](https://aclanthology.org/2026.findings-eacl.213/) *— 2026 · Findings of the Association for Computational Linguistics: EACL 2026*
10. [DiFRa: A Unified Framework for Harmonizing Semantic Diversity and Factual Consistency in Question-Answer Generation](https://aclanthology.org/2026.findings-acl.1493/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
11. [Do We Always Need Query-Level Workflows? Rethinking Agentic Workflow Generation for Multi-Agent Systems](https://aclanthology.org/2026.findings-acl.254/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
12. [DynamixSFT: Dynamic Mixture Optimization of Instruction Tuning Collections](https://aclanthology.org/2026.findings-acl.1972/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
13. [FormulaReasoning: A Dataset for Formula-Based Numerical Reasoning](https://aclanthology.org/2026.findings-acl.1580/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
14. [FusionFlow: Enabling Deep Structural Exploration for Automated Agentic Workflow Generation](https://aclanthology.org/2026.acl-long.1278/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [Hunyuan-A13B Technical Report](https://arxiv.org/abs/2609.27284) *— 2026 · arXiv:2609.27284*
16. [Jamendo-MT-QA: A Benchmark for Multi-Track Comparative Music Question Answering](https://aclanthology.org/2026.findings-acl.125/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
17. [Knowledge-Infused Multi-Bit Watermarking for RAG Knowledge Bases](https://aclanthology.org/2026.findings-acl.1066/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
18. [Large-Scale Diverse Synthesis for Mid-Training](https://aclanthology.org/2026.findings-acl.814/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
19. [LinkQA: Synthesizing Diverse QA from Multiple Seeds Strongly Linked by Knowledge Points](https://aclanthology.org/2026.acl-long.1036/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
20. [MEUR: A Benchmark for Evaluating Vision-Language Models on Multimodal Event Understanding and Reasoning](https://aclanthology.org/2026.lrec-1.134/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
21. [MiMo-V2-Flash Technical Report](https://arxiv.org/abs/2601.02780) *— 2026 · arXiv:2601.02780*
22. [MoNaCo: More Natural and Complex Questions for Reasoning Across Dozens of Documents](https://aclanthology.org/2026.tacl-1.2/) *— 2026 · Transactions of the Association for Computational Linguistics*
23. [Nature-Inspired Population-Based Evolution of Large Language Models](https://aclanthology.org/2026.acl-long.2044/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
24. [PBEBench: A Multi-Step Programming by Examples Reasoning Benchmark inspired by Historical Linguistics](https://aclanthology.org/2026.findings-acl.432/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
25. [Parallelism and Generation Order in Masked Diffusion Language Models: Limits Today, Potential Tomorrow](https://aclanthology.org/2026.findings-acl.357/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
26. [Preference Optimization for Review Question Generation Improves Writing Quality](https://aclanthology.org/2026.findings-acl.1256/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
27. [SciPedia: Unlocking the Value of Scientific Data for Pre-training](https://aclanthology.org/2026.acl-long.2181/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
28. [Think in Sentences: Explicit Sentence Boundaries Enhance Language Model''s Capabilities](https://aclanthology.org/2026.acl-long.104/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
29. [Universally Empowering Zeroth-Order Optimization via Adaptive Layer-wise Sampling](https://aclanthology.org/2026.findings-acl.1999/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
30. [2 OLMo 2 Furious](https://arxiv.org/abs/2501.00656) *— 2025 · arXiv:2501.00656*
31. [A Survey of Post-Training Scaling in Large Language Models](https://aclanthology.org/2025.acl-long.140/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
32. [A Systematic Survey of Automatic Prompt Optimization Techniques](https://aclanthology.org/2025.emnlp-main.1681/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
33. [AUTOLAW: Enhancing Legal Compliance in Large Language Models via Case Law Generation and Jury-Inspired Deliberation](https://arxiv.org/abs/2505.14015) *— 2025 · arXiv:2505.14015*
34. [Accurate INT8 Training Through Dynamic Block-Level Fallback](https://arxiv.org/abs/2503.08040) *— 2025 · arXiv:2503.08040*
35. [Analyzing Uncertainty of LLM-as-a-Judge: Interval Evaluations with Conformal Prediction](https://aclanthology.org/2025.emnlp-main.569/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
36. [Anchored Preference Optimization and Contrastive Revisions: Addressing Underspecification in Alignment](https://aclanthology.org/2025.tacl-1.22/) *— 2025 · Transactions of the Association for Computational Linguistics*
37. [CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning](https://arxiv.org/abs/2503.13517) *— 2025 · arXiv:2503.13517*
38. [Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generation](https://arxiv.org/abs/2502.15734) *— 2025 · arXiv:2502.15734*
39. [Code to Think, Think to Code: A Survey on Code-Enhanced Reasoning and Reasoning-Driven Code Intelligence in LLMs](https://aclanthology.org/2025.emnlp-main.130/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
40. [CodeI/O: Condensing Reasoning Patterns via Code Input-Output Prediction](https://arxiv.org/abs/2502.07316) *— 2025 · arXiv:2502.07316*
41. [CompassVerifier: A Unified and Robust Verifier for LLMs Evaluation and Outcome Reward](https://aclanthology.org/2025.emnlp-main.1698/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
42. [Condor: Enhance LLM Alignment with Knowledge-Driven Data Synthesis and Refinement](https://aclanthology.org/2025.acl-long.1091/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [Cool-Fusion: Fuse Large Language Models without Training](https://aclanthology.org/2025.acl-long.521/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
44. [DiPT: Enhancing LLM Reasoning through Diversified Perspective-Taking](https://aclanthology.org/2025.findings-naacl.356/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
45. [Do Large Language Model Benchmarks Test Reliability?](https://arxiv.org/abs/2502.03461) *— 2025 · arXiv:2502.03461*
46. [From LLM Reasoning to Autonomous AI Agents: A Comprehensive Review](https://arxiv.org/abs/2504.19678) *— 2025 · arXiv:2504.19678*
47. [Gemma 3 Technical Report](https://arxiv.org/abs/2503.19786) *— 2025 · arXiv:2503.19786*
48. [Generating Complex Question Decompositions in the Face of Distribution Shifts](https://aclanthology.org/2025.naacl-long.55/) *— 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)*
49. [Gödel Agent: A Self-Referential Agent Framework for Recursively Self-Improvement](https://aclanthology.org/2025.acl-long.1354/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
50. [Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient Zeroth-order LLM Fine-tuning](https://arxiv.org/abs/2502.03304) *— 2025 · arXiv:2502.03304*
51. [HoPE: A Novel Positional Encoding Without Long-Term Decay for Enhanced Context Awareness and Extrapolation](https://aclanthology.org/2025.acl-long.1123/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
52. [How to Leverage Digit Embeddings to Represent Numbers?](https://aclanthology.org/2025.coling-main.514/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
53. [Humanity's Last Exam](https://arxiv.org/abs/2501.14249) *— 2025 · arXiv:2501.14249*
54. [Hunyuan-TurboS: Advancing Large Language Models through Mamba-Transformer Synergy and Adaptive Chain-of-Thought](https://arxiv.org/abs/2505.15431) *— 2025 · arXiv:2505.15431*
55. [Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2509.02350) *— 2025 · arXiv:2509.02350*
56. [Improving Chain-of-Thought Reasoning via Quasi-Symbolic Abstractions](https://aclanthology.org/2025.acl-long.843/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
57. [JustLogic: A Comprehensive Benchmark for Evaluating Deductive Reasoning in Large Language Models](https://arxiv.org/abs/2501.14851) *— 2025 · arXiv:2501.14851*
58. [Kimi K2: Open Agentic Intelligence](https://arxiv.org/abs/2507.20534) *— 2025 · arXiv:2507.20534*
59. [LogicPro: Improving Complex Logical Reasoning via Program-Guided Learning](https://aclanthology.org/2025.acl-long.1270/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
60. [MIRAGE: A Metric-Intensive Benchmark for Retrieval-Augmented Generation Evaluation](https://aclanthology.org/2025.findings-naacl.157/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
61. [MiMo-VL Technical Report](https://arxiv.org/abs/2506.03569) *— 2025 · arXiv:2506.03569*
62. [MiMo: Unlocking the Reasoning Potential of Language Model -- From Pretraining to Posttraining](https://arxiv.org/abs/2505.07608) *— 2025 · arXiv:2505.07608*
63. [MiniLongBench: The Low-cost Long Context Understanding Benchmark for Large Language Models](https://aclanthology.org/2025.acl-long.560/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
64. [MiniMax-01: Scaling Foundation Models with Lightning Attention](https://arxiv.org/abs/2501.08313) *— 2025 · arXiv:2501.08313*
65. [MoM: Linear Sequence Modeling with Mixture-of-Memories](https://arxiv.org/abs/2502.13685) *— 2025 · arXiv:2502.13685*
66. [More Data or Better Data? A Critical Analysis of Data Selection and Synthesis for Mathematical Reasoning](https://aclanthology.org/2025.emnlp-industry.43/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: Industry Track*
67. [NOSA: Native and Offloadable Sparse Attention](https://arxiv.org/abs/2510.13602) *— 2025 · arXiv:2510.13602*
68. [Native Sparse Attention: Hardware-Aligned and Natively Trainable Sparse Attention](https://arxiv.org/abs/2502.11089) *— 2025 · arXiv:2502.11089*
69. [Olmo 3](https://arxiv.org/abs/2512.13961) *— 2025 · arXiv:2512.13961*
70. [Pangu Pro MoE: Mixture of Grouped Experts for Efficient Sparsity](https://arxiv.org/abs/2505.21411) *— 2025 · arXiv:2505.21411*
71. [Pangu Ultra: Pushing the Limits of Dense Large Language Models on Ascend NPUs](https://arxiv.org/abs/2504.07866) *— 2025 · arXiv:2504.07866*
72. [QuZO: Quantized Zeroth-Order Fine-Tuning for Large Language Models](https://aclanthology.org/2025.emnlp-main.271/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
73. [Reasoning with Latent Thoughts: On the Power of Looped Transformers](https://arxiv.org/abs/2502.17416) *— 2025 · arXiv:2502.17416*
74. [STEER-BENCH: A Benchmark for Evaluating the Steerability of Large Language Models](https://aclanthology.org/2025.emnlp-main.925/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
75. [SageAttention3: Microscaling FP4 Attention for Inference and An Exploration of 8-Bit Training](https://arxiv.org/abs/2505.11594) *— 2025 · arXiv 2025*
76. [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)*
77. [Sketch-of-Thought: Efficient LLM Reasoning with Adaptive Cognitive-Inspired Sketching](https://aclanthology.org/2025.emnlp-main.1236/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
78. [SmolLM2: When Smol Goes Big -- Data-Centric Training of a Small Language Model](https://arxiv.org/abs/2502.02737) *— 2025 · arXiv:2502.02737*
79. [Step Guided Reasoning: Improving Mathematical Reasoning using Guidance Generation and Step Reasoning](https://aclanthology.org/2025.emnlp-main.1068/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
80. [Structured Definitions and Segmentations for Legal Reasoning in LLMs: A Study on Indian Legal Data](https://arxiv.org/abs/2511.20669) *— 2025 · arXiv:2511.20669*
81. [Supportiveness-based Knowledge Rewriting for Retrieval-augmented Language Modeling](https://aclanthology.org/2025.findings-naacl.148/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
82. [Towards Data Contamination Detection for Modern Large Language Models: Limitations, Inconsistencies, and Oracle Challenges](https://aclanthology.org/2025.coling-main.338/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
83. [Towards the Law of Capacity Gap in Distilling Language Models](https://aclanthology.org/2025.acl-long.1097/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
84. [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*
85. [VerIF: Verification Engineering for Reinforcement Learning in Instruction Following](https://aclanthology.org/2025.emnlp-main.1542/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
86. [Weak-for-Strong: Training Weak Meta-Agent to Harness Strong Executors](https://arxiv.org/abs/2504.04785) *— 2025 · arXiv:2504.04785*
87. [Which of These Best Describes Multiple Choice Evaluation with LLMs? A) Forced B) Flawed C) Fixable D) All of the Above](https://aclanthology.org/2025.acl-long.169/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [olmOCR: Unlocking Trillions of Tokens in PDFs with Vision Language Models](https://arxiv.org/abs/2502.18443) *— 2025 · arXiv:2502.18443*
89. [AFlow: Automating Agentic Workflow Generation](https://arxiv.org/abs/2410.10762) *— 2024 · arXiv:2410.10762*
90. [Adding Error Bars to Evals: A Statistical Approach to Language Model Evaluations](https://arxiv.org/abs/2411.00640) *— 2024 · arXiv:2411.00640*
91. [AgentInstruct: Toward Generative Teaching with Agentic Flows](https://arxiv.org/abs/2407.03502) *— 2024 · arXiv:2407.03502*
92. [AssistantBench: Can Web Agents Solve Realistic and Time-Consuming Tasks?](https://aclanthology.org/2024.emnlp-main.505/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
93. [Automated Design of Agentic Systems](https://arxiv.org/abs/2408.08435) *— 2024 · arXiv:2408.08435*
94. [Benchmarking Large Language Models on CFLUE - A Chinese Financial Language Understanding Evaluation Dataset](https://aclanthology.org/2024.findings-acl.337/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
95. [CIF-Bench: A Chinese Instruction-Following Benchmark for Evaluating the Generalizability of Large Language Models](https://aclanthology.org/2024.findings-acl.739/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
96. [Can Long-Context Language Models Subsume Retrieval, RAG, SQL, and More?](https://arxiv.org/abs/2406.13121) *— 2024 · arXiv:2406.13121*
97. [Competition-Level Problems are Effective LLM Evaluators](https://aclanthology.org/2024.findings-acl.803/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
98. [Continual Learning of Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2404.16789) *— 2024 · arXiv:2404.16789*
99. [DBQR-QA: A Question Answering Dataset on a Hybrid of Database Querying and Reasoning](https://aclanthology.org/2024.findings-acl.900/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
100. [Data Diversity Matters for Robust Instruction Tuning](https://aclanthology.org/2024.findings-emnlp.195/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*

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