---
title: "Who cites The NarrativeQA Reading Comprehension Challenge…"
description: "The papers of the Elamur Research library that cite «The NarrativeQA Reading Comprehension Challenge», found in their bibliographies."
canonical: "https://research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/2/"
---
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# Who cites «The NarrativeQA Reading Comprehension Challenge» · page 2

435 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/kocisky-2018-narrativeqa-reading-comprehension-challenge/)

Pages: [1](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/) · **2** · [3](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/3/) · [4](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/4/) · [5](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/5/)

1. [OpeNLGauge: An Explainable Metric for NLG Evaluation with Open-Weights LLMs](https://aclanthology.org/2025.inlg-main.19/) *— 2025 · Proceedings of the 18th International Natural Language Generation Conference*
2. [PECAN: LLM-Guided Dynamic Progress Control with Attention-Guided Hierarchical Weighted Graph for Long-Document QA](https://aclanthology.org/2025.findings-acl.689/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
3. [ParallelComp: Parallel Long-Context Compressor for Length Extrapolation](https://arxiv.org/abs/2502.14317) *— 2025 · arXiv:2502.14317*
4. [PeerQA: A Scientific Question Answering Dataset from Peer Reviews](https://aclanthology.org/2025.naacl-long.22/) *— 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)*
5. [QwenLong-L1.5: Post-Training Recipe for Long-Context Reasoning and Memory Management](https://arxiv.org/abs/2512.12967) *— 2025 · arXiv:2512.12967*
6. [QwenLong-L1: Towards Long-Context Large Reasoning Models with Reinforcement Learning](https://arxiv.org/abs/2505.17667) *— 2025 · arXiv:2505.17667*
7. [R$^3$Mem: Bridging Memory Retention and Retrieval via Reversible Compression](https://aclanthology.org/2025.findings-acl.235/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
8. [RAG-Instruct: Boosting LLMs with Diverse Retrieval-Augmented Instructions](https://aclanthology.org/2025.emnlp-main.192/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
9. [ReSSFormer: A Recursive Sparse Structured Transformer for Scalable and Long-Context Reasoning](https://arxiv.org/abs/2510.01585) *— 2025 · arXiv:2510.01585*
10. [SCOP: Evaluating the Comprehension Process of Large Language Models from a Cognitive View](https://aclanthology.org/2025.acl-long.852/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
11. [SKETCH: Structured Knowledge Enhanced Text Comprehension for Holistic Retrieval](https://aclanthology.org/2025.genaik-1.4/) *— 2025 · Proceedings of the Workshop on Generative AI and Knowledge Graphs (GenAIK)*
12. [SSMLoRA: Enhancing Low-Rank Adaptation with State Space Model](https://aclanthology.org/2025.naacl-long.230/) *— 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)*
13. [SYNC: A Synthetic Long-Context Understanding Benchmark for Controlled Comparisons of Model Capabilities](https://aclanthology.org/2025.emnlp-main.1707/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
14. [Self-Taught Agentic Long Context Understanding](https://aclanthology.org/2025.acl-long.275/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
15. [SemEval 2025 Task 10: Multilingual Characterization and Extraction of Narratives from Online News](https://aclanthology.org/2025.semeval-1.331/) *— 2025 · Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)*
16. [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)*
17. [SparseServe: Unlocking Parallelism for Dynamic Sparse Attention in Long-Context LLM Serving](https://arxiv.org/abs/2509.24626) *— 2025 · arXiv:2509.24626*
18. [Stronger Baselines for Retrieval-Augmented Generation with Long-Context Language Models](https://aclanthology.org/2025.emnlp-main.1656/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
19. [The Essence of Contextual Understanding in Theory of Mind: A Study on Question Answering with Story Characters](https://aclanthology.org/2025.acl-long.1103/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
20. [The Threat of PROMPTS in Large Language Models: A System and User Prompt Perspective](https://aclanthology.org/2025.findings-acl.675/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
21. [Think Both Ways: Teacher-Student Bidirectional Reasoning Enhances MCQ Generation and Distractor Quality](https://aclanthology.org/2025.findings-acl.432/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
22. [Towards Multi-Document Question Answering in Scientific Literature: Pipeline, Dataset, and Evaluation](https://aclanthology.org/2025.findings-emnlp.576/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
23. [UNLEARN Efficient Removal of Knowledge in Large Language Models](https://aclanthology.org/2025.findings-naacl.405/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
24. [Understanding Bottlenecks for Efficiently Serving LLM Inference With KV Offloading](https://arxiv.org/abs/2601.19910) *— 2025 · arXiv:2601.19910*
25. [UniRAG: Unified Query Understanding Method for Retrieval Augmented Generation](https://aclanthology.org/2025.acl-long.693/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
26. [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*
27. [VisDoM: Multi-Document QA with Visually Rich Elements Using Multimodal Retrieval-Augmented Generation](https://aclanthology.org/2025.naacl-long.310/) *— 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)*
28. [What Has Been Lost with Synthetic Evaluation?](https://aclanthology.org/2025.findings-emnlp.526/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
29. [What We Learned from Continually Training Minerva: A Case Study on Italian](https://aclanthology.org/2025.clicit-1.72/) *— 2025 · Proceedings of the Eleventh Italian Conference on Computational Linguistics (CLiC-it 2025)*
30. [When Long Helps Short: How Context Length in Supervised Fine-tuning Affects Behavior of Large Language Models](https://aclanthology.org/2025.emnlp-main.522/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
31. [You Only Use Reactive Attention Slice When Retrieving From Long Context](https://aclanthology.org/2025.findings-emnlp.1125/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
32. [$ınfty$Bench: Extending Long Context Evaluation Beyond 100K Tokens](https://aclanthology.org/2024.acl-long.814/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
33. [A + B: A General Generator-Reader Framework for Optimizing LLMs to Unleash Synergy Potential](https://aclanthology.org/2024.findings-acl.219/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
34. [A Human-Inspired Reading Agent with Gist Memory of Very Long Contexts](https://arxiv.org/abs/2402.09727) *— 2024 · arXiv:2402.09727*
35. [A Simple and Effective $L_2$ Norm-Based Strategy for KV Cache Compression](https://aclanthology.org/2024.emnlp-main.1027/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
36. [A Survey on Detection of LLMs-Generated Content](https://aclanthology.org/2024.findings-emnlp.572/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
37. [A Tale of Two Revisions: Summarizing Changes Across Document Versions](https://aclanthology.org/2024.findings-acl.190/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
38. [An Empirical Study of Mamba-based Language Models](https://arxiv.org/abs/2406.07887) *— 2024 · arXiv:2406.07887*
39. [Batch-Max: Higher LLM Throughput using Larger Batch Sizes and KV Cache Compression](https://arxiv.org/abs/2412.05693) *— 2024 · arXiv:2412.05693*
40. [Beyond Natural Language: LLMs Leveraging Alternative Formats for Enhanced Reasoning and Communication](https://aclanthology.org/2024.findings-emnlp.623/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
41. [Bridging Context Gaps: Leveraging Coreference Resolution for Long Contextual Understanding](https://arxiv.org/abs/2410.01671) *— 2024 · arXiv:2410.01671*
42. [CLongEval: A Chinese Benchmark for Evaluating Long-Context Large Language Models](https://aclanthology.org/2024.findings-emnlp.230/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
43. [CORM: Cache Optimization with Recent Message for Large Language Model Inference](https://arxiv.org/abs/2404.15949) *— 2024 · arXiv:2404.15949*
44. [Can GPT Redefine Medical Understanding? Evaluating GPT on Biomedical Machine Reading Comprehension](https://aclanthology.org/2024.bionlp-1.20/) *— 2024 · Proceedings of the 23rd Workshop on Biomedical Natural Language Processing*
45. [Chain of Agents: Large Language Models Collaborating on Long-Context Tasks](https://arxiv.org/abs/2406.02818) *— 2024 · arXiv:2406.02818*
46. [ChatQA: Surpassing GPT-4 on Conversational QA and RAG](https://arxiv.org/abs/2401.10225) *— 2024 · arXiv:2401.10225*
47. [Chunk, Align, Select: A Simple Long-sequence Processing Method for Transformers](https://aclanthology.org/2024.acl-long.729/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [ContraDoc: Understanding Self-Contradictions in Documents with Large Language Models](https://aclanthology.org/2024.naacl-long.362/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
49. [CritiPrefill: A Segment-wise Criticality-based Approach for Prefilling Acceleration in LLMs](https://arxiv.org/abs/2409.12490) *— 2024 · arXiv:2409.12490*
50. [DLUE: Benchmarking Document Language Understanding](https://aclanthology.org/2024.ccl-1.97/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)*
51. [Do Large Language Models Understand Conversational Implicature- A case study with a Chinese sitcom](https://aclanthology.org/2024.ccl-1.98/) *— 2024 · Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)*
52. [DocFinQA: A Long-Context Financial Reasoning Dataset](https://aclanthology.org/2024.acl-short.42/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
53. [Evaluating Character Understanding of Large Language Models via Character Profiling from Fictional Works](https://aclanthology.org/2024.emnlp-main.456/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
54. [Exploring Scientific Hypothesis Generation with Mamba](https://aclanthology.org/2024.nlp4science-1.17/) *— 2024 · Proceedings of the 1st Workshop on NLP for Science (NLP4Science)*
55. [Extending Context Window of Large Language Models via Semantic Compression](https://aclanthology.org/2024.findings-acl.306/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
56. [Fine-Grained Modeling of Narrative Context: A Coherence Perspective via Retrospective Questions](https://aclanthology.org/2024.acl-long.317/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
57. [FlashRAG: A Modular Toolkit for Efficient Retrieval-Augmented Generation Research](https://arxiv.org/abs/2405.13576) *— 2024 · arXiv:2405.13576*
58. [FragRel: Exploiting Fragment-level Relations in the External Memory of Large Language Models](https://aclanthology.org/2024.findings-acl.968/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
59. [GPT vs RETRO: Exploring the Intersection of Retrieval and Parameter-Efficient Fine-Tuning](https://aclanthology.org/2024.emnlp-main.1081/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
60. [Gated Delta Networks: Improving Mamba2 with Delta Rule](https://arxiv.org/abs/2412.06464) *— 2024 · arXiv:2412.06464*
61. [GraphReader: Building Graph-based Agent to Enhance Long-Context Abilities of Large Language Models](https://aclanthology.org/2024.findings-emnlp.746/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
62. [Grounding Language Model with Chunking-Free In-Context Retrieval](https://aclanthology.org/2024.acl-long.71/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [HELMET: How to Evaluate Long-Context Language Models Effectively and Thoroughly](https://arxiv.org/abs/2410.02694) *— 2024 · arXiv:2410.02694*
64. [HumBEL: A Human-in-the-Loop Approach for Evaluating Demographic Factors of Language Models in Human-Machine Conversations](https://aclanthology.org/2024.eacl-long.68/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
65. [Instruction Pre-Training: Language Models are Supervised Multitask Learners](https://aclanthology.org/2024.emnlp-main.148/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
66. [Interpreting Themes from Educational Stories](https://aclanthology.org/2024.lrec-main.805/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
67. [Is It Really Long Context if All You Need Is Retrieval? Towards Genuinely Difficult Long Context NLP](https://aclanthology.org/2024.emnlp-main.924/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
68. [Jamba: A Hybrid Transformer-Mamba Language Model](https://arxiv.org/abs/2403.19887) *— 2024 · arXiv:2403.19887*
69. [Knowledge Conflicts for LLMs: A Survey](https://aclanthology.org/2024.emnlp-main.486/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
70. [L-Eval: Instituting Standardized Evaluation for Long Context Language Models](https://aclanthology.org/2024.acl-long.776/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
71. [LFED: A Literary Fiction Evaluation Dataset for Large Language Models](https://aclanthology.org/2024.lrec-main.915/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
72. [LLM-Inference-Bench: Inference Benchmarking of Large Language Models on AI Accelerators](https://arxiv.org/abs/2411.00136) *— 2024 · arXiv:2411.00136*
73. [LLM-as-a-Coauthor: Can Mixed Human-Written and Machine-Generated Text Be Detected?](https://aclanthology.org/2024.findings-naacl.29/) *— 2024 · Findings of the Association for Computational Linguistics: NAACL 2024*
74. [Label-Efficient Model Selection for Text Generation](https://aclanthology.org/2024.acl-long.456/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
75. [Landmark Embedding: A Chunking-Free Embedding Method For Retrieval Augmented Long-Context Large Language Models](https://aclanthology.org/2024.acl-long.180/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
76. [Large Language Models as Foundations for Next-Gen Dense Retrieval: A Comprehensive Empirical Assessment](https://aclanthology.org/2024.emnlp-main.80/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
77. [Linearizing Large Language Models](https://arxiv.org/abs/2405.06640) *— 2024 · arXiv:2405.06640*
78. [Long-Context Language Modeling with Parallel Context Encoding](https://aclanthology.org/2024.acl-long.142/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
79. [LongAlign: A Recipe for Long Context Alignment of Large Language Models](https://arxiv.org/abs/2401.18058) *— 2024 · arXiv:2401.18058*
80. [LongBench v2: Towards Deeper Understanding and Reasoning on Realistic Long-context Multitasks](https://arxiv.org/abs/2412.15204) *— 2024 · arXiv:2412.15204*
81. [LongEmbed: Extending Embedding Models for Long Context Retrieval](https://aclanthology.org/2024.emnlp-main.47/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
82. [LumberChunker: Long-Form Narrative Document Segmentation](https://aclanthology.org/2024.findings-emnlp.377/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
83. [M3-Embedding: Multi-Linguality, Multi-Functionality, Multi-Granularity Text Embeddings Through Self-Knowledge Distillation](https://arxiv.org/abs/2402.03216) *— 2024 · arXiv:2402.03216*
84. [Make Your LLM Fully Utilize the Context](https://arxiv.org/abs/2404.16811) *— 2024 · arXiv:2404.16811*
85. [MetaMetrics: Calibrating Metrics For Generation Tasks Using Human Preferences](https://arxiv.org/abs/2410.02381) *— 2024 · arXiv:2410.02381*
86. [Michelangelo: Long Context Evaluations Beyond Haystacks via Latent Structure Queries](https://arxiv.org/abs/2409.12640) *— 2024 · arXiv:2409.12640*
87. [MovieSum: An Abstractive Summarization Dataset for Movie Screenplays](https://aclanthology.org/2024.findings-acl.239/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
88. [NACL: A General and Effective KV Cache Eviction Framework for LLM at Inference Time](https://aclanthology.org/2024.acl-long.428/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
89. [Neurocache: Efficient Vector Retrieval for Long-range Language Modeling](https://aclanthology.org/2024.naacl-long.50/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
90. [NovelQA: Benchmarking Question Answering on Documents Exceeding 200K Tokens](https://arxiv.org/abs/2403.12766) *— 2024 · arXiv:2403.12766*
91. [On Narrative Question Answering Skills](https://aclanthology.org/2024.naacl-short.73/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)*
92. [On the Adaptation of Unlimiformer for Decoder-Only Transformers](https://aclanthology.org/2024.lrec-main.1085/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
93. [PEARL: Prompting Large Language Models to Plan and Execute Actions Over Long Documents](https://aclanthology.org/2024.eacl-long.29/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
94. [PEDANTS: Cheap but Effective and Interpretable Answer Equivalence](https://aclanthology.org/2024.findings-emnlp.548/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
95. [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)*
96. [Plot Retrieval as an Assessment of Abstract Semantic Association](https://aclanthology.org/2024.acl-srw.57/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 4: Student Research Workshop)*
97. [PyramidKV: Dynamic KV Cache Compression based on Pyramidal Information Funneling](https://arxiv.org/abs/2406.02069) *— 2024 · arXiv:2406.02069*
98. [Quest: Query-Aware Sparsity for Efficient Long-Context LLM Inference](https://arxiv.org/abs/2406.10774) *— 2024 · arXiv:2406.10774*
99. [RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answering](https://aclanthology.org/2024.emnlp-main.249/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
100. [RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval](https://arxiv.org/abs/2401.18059) *— 2024 · arXiv:2401.18059*

Pages: [1](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/) · **2** · [3](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/3/) · [4](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/4/) · [5](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/page/5/)