---
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/"
---
[Elamur Research](https://md.research.elamur.ai/free/en/) · [this page in HTML](https://research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/cited-by/)

[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [The NarrativeQA Reading Comprehension Challenge](https://md.research.elamur.ai/free/en/papers/kocisky-2018-narrativeqa-reading-comprehension-challenge/) / cited by

# Who cites «The NarrativeQA Reading Comprehension Challenge»

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

1. [A Survey on Evaluation of LLM-based Agents](https://aclanthology.org/2026.findings-acl.1330/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
2. [AQPIM: Breaking the PIM Capacity Wall for LLMs with In-Memory Activation Quantization](https://arxiv.org/abs/2604.18137) *— 2026 · arXiv:2604.18137*
3. [ARK: Answer-Centric Retriever Tuning via KG-augmented Curriculum Learning](https://aclanthology.org/2026.acl-long.371/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
4. [BRIEF-Pro: Universal Context Compression with Short-to-Long Synthesis for Fast and Accurate Multi-Hop Reasoning](https://aclanthology.org/2026.findings-acl.696/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
5. [Beyond Chunking: Discourse-Aware Hierarchical Retrieval for Long Document Question Answering](https://aclanthology.org/2026.acl-long.829/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
6. [BudgetDraft: Acceptance-Aware Multi-View Training for Sparse-KV Speculative Decoding](https://arxiv.org/abs/2606.00144) *— 2026 · arXiv:2606.00144*
7. [Chunks as Arms: Multi-Armed Bandit-Guided Sampling for Long-Context LLM Preference Optimization](https://aclanthology.org/2026.acl-long.161/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
8. [CoMeT: Collaborative Memory Transformer for Efficient Long Context Modeling](https://aclanthology.org/2026.acl-long.1779/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
9. [Development of Serbian QA Datasets through Prompt-Based Generation and Human Validation](https://aclanthology.org/2026.lrec-1.267/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
10. [DiscoRAG: A Discourse-Aware Agent for Query-Based Summarization of Long Documents](https://aclanthology.org/2026.lrec-1.162/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
11. [EduBench: A Portuguese Benchmark for Open-Ended Discursive Question Answering](https://aclanthology.org/2026.lrec-1.360/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
12. [Enhancing and Evaluating Tabular Models on the Fly via Synthetic Question–Answer Generation](https://aclanthology.org/2026.lrec-1.421/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
13. [FlashRT: Towards Computationally and Memory Efficient Red-Teaming for Prompt Injection and Knowledge Corruption](https://arxiv.org/abs/2604.28157) *— 2026 · arXiv:2604.28157*
14. [ForkKV: Scaling Multi-LoRA Agent Serving via Copy-on-Write Disaggregated KV Cache](https://arxiv.org/abs/2604.06370) *— 2026 · arXiv:2604.06370*
15. [GMSA: Enhancing Context Compression via Group Merging and Layer Semantic Alignment](https://aclanthology.org/2026.acl-long.1324/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
16. [HSS-Synth: Humanities and Social Sciences Data Synthesis for LLMs](https://aclanthology.org/2026.findings-acl.1880/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
17. [HiChunk: Evaluating and Enhancing Retrieval Augmented Generation with Hierarchical Chunking](https://aclanthology.org/2026.acl-long.1372/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [Hierarchical Token Prepending: Enhancing Information Flow in Decoder-based LLM Embeddings](https://aclanthology.org/2026.acl-long.870/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
19. [INDAQA2 - A Large Italian Narrative QA Benchmark: A CALAMITA 2026 Challenge](https://aclanthology.org/2026.evalita-1.71/) *— 2026 · Proceedings of the Ninth Evaluation Campaign of Natural Language Processing and Speech Tools for Italian. Final Workshop (EVALITA 2026)*
20. [Instella-MoE Technical Report](https://arxiv.org/abs/2609.00791) *— 2026 · arXiv:2609.00791*
21. [Intra-Prompt Parallel Decoding for Common-Context Question Answering](https://arxiv.org/abs/2609.05707) *— 2026 · arXiv:2609.05707*
22. [LFQA-HP-1M: A Large-Scale Human Preference Dataset for Long-Form Question Answering](https://aclanthology.org/2026.lrec-1.425/) *— 2026 · Proceedings of the Fifteenth Language Resources and Evaluation Conference*
23. [Learning Optimal Message Representations for Agentic Communication](https://aclanthology.org/2026.findings-acl.1441/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
24. [LiveLongBench: Tackling Long-Context Understanding for Spoken Texts from Live Streams](https://aclanthology.org/2026.findings-acl.1485/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
25. [LoSA: Locality Aware Sparse Attention for Block-Wise Diffusion Language Models](https://arxiv.org/abs/2604.12056) *— 2026 · arXiv:2604.12056*
26. [MSA: Memory Sparse Attention for Efficient End-to-End Memory Model Scaling to 100M Tokens](https://arxiv.org/abs/2603.23516) *— 2026 · arXiv:2603.23516*
27. [Mask Tokens as Prophet: Fine-Grained Cache Eviction for Efficient dLLM Inference](https://aclanthology.org/2026.findings-acl.170/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
28. [MemAdapter: Fast Alignment across Agent Memory Paradigms via Generative Subgraph Retrieval](https://arxiv.org/abs/2602.08369) *— 2026 · arXiv:2602.08369*
29. [MemSearch-o1: Empowering Large Language Models with Reasoning-Aligned Memory Growth in Agentic Search](https://aclanthology.org/2026.acl-long.41/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
30. [Memory Matters More: Event-Centric Memory as a Logic Map for Agent Searching and Reasoning](https://aclanthology.org/2026.findings-acl.1123/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
31. [More Than Efficiency: Embedding Compression Improves Domain Adaptation in Dense Retrieval](https://aclanthology.org/2026.surgellm-1.24/) *— 2026 · Proceedings of the First Workshop on Structured Understanding, Retrieval, and Generation in the LLM Era (SURGeLLM 2026)*
32. [Optimizing Native Sparse Attention with Latent Attention and Local Global Alternating Strategies](https://aclanthology.org/2026.findings-acl.695/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
33. [Predicting Future Utility: Global Combinatorial Optimization for Task-Agnostic KV Cache Eviction](https://arxiv.org/abs/2602.08585) *— 2026 · arXiv:2602.08585*
34. [ReFreeKV: Towards Threshold-Free KV Cache Compression](https://aclanthology.org/2026.findings-acl.1541/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
35. [Read As Human: Compressing Context via Parallelizable Close Reading and Skimming](https://aclanthology.org/2026.acl-long.1309/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
36. [SARA: Selective and Adaptive Retrieval-augmented Generation with Context Compression](https://aclanthology.org/2026.acl-long.661/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
37. [Self-Consistency Falls Short! The Adverse Effects of Positional Bias on Long-Context Problems](https://aclanthology.org/2026.tacl-1.15/) *— 2026 · Transactions of the Association for Computational Linguistics*
38. [Situated Embedding Models for Context-Aware Dense Retrieval](https://aclanthology.org/2026.acl-short.5/) *— 2026 · Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)*
39. [TempoKV: Timely Staging of LLM KV Caches for Memory-Semantic Flash](https://arxiv.org/abs/2609.35065) *— 2026 · arXiv:2609.35065*
40. [TopoRAG: Graph-based RAG via Topology-aware Approximate Nearest Neighbor Search](https://aclanthology.org/2026.findings-acl.1703/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
41. [$\textitL-CiteEval$: A Suite for Evaluating Fidelity of Long-context Models](https://aclanthology.org/2025.acl-long.263/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
42. [A Comprehensive Survey on Long Context Language Modeling](https://arxiv.org/abs/2503.17407) *— 2025 · arXiv:2503.17407*
43. [Accelerating LLM Inference with Precomputed Query Storage](https://arxiv.org/abs/2509.25919) *— 2025 · arXiv:2509.25919*
44. [Adapting Large Language Models for Movie Domain with Narrative Understanding Tasks](https://aclanthology.org/2025.conll-1.13/) *— 2025 · Proceedings of the 29th Conference on Computational Natural Language Learning*
45. [Advancing Question Generation with Joint Narrative and Difficulty Control](https://aclanthology.org/2025.bea-1.46/) *— 2025 · Proceedings of the 20th Workshop on Innovative Use of NLP for Building Educational Applications (BEA 2025)*
46. [BABILong-ITA: A New Benchmark for Testing Large Language Models Effective Context Length and a Context Extension Method](https://aclanthology.org/2025.clicit-1.105/) *— 2025 · Proceedings of the Eleventh Italian Conference on Computational Linguistics (CLiC-it 2025)*
47. [Boosting Long-Context Information Seeking via Query-Guided Activation Refilling](https://aclanthology.org/2025.acl-long.465/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
48. [Brevity is the soul of sustainability: Characterizing LLM response lengths](https://aclanthology.org/2025.findings-acl.1125/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
49. [BudgetMem: Training-Free Selective Memory for Cost-Efficient Long-Context Processing in Language Models](https://arxiv.org/abs/2511.04919) *— 2025 · arXiv:2511.04919*
50. [CURIE: Evaluating LLMs On Multitask Scientific Long Context Understanding and Reasoning](https://arxiv.org/abs/2503.13517) *— 2025 · arXiv:2503.13517*
51. [Can Stories Help LLMs Reason? Curating Information Space Through Narrative](https://aclanthology.org/2025.analogyangle-1.8/) *— 2025 · Proceedings of the 2nd Workshop on Analogical Abstraction in Cognition, Perception, and Language (Analogy-Angle II)*
52. [Characterizing LLM Inference Energy-Performance Tradeoffs across Workloads and GPU Scaling](https://arxiv.org/abs/2501.08219) *— 2025 · arXiv:2501.08219*
53. [ChunkKV: Semantic-Preserving KV Cache Compression for Efficient Long-Context LLM Inference](https://arxiv.org/abs/2502.00299) *— 2025 · arXiv:2502.00299*
54. [ClusterAttn: KV Cache Compression under Intrinsic Attention Clustering](https://aclanthology.org/2025.acl-long.703/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
55. [Context is Gold to find the Gold Passage: Evaluating and Training Contextual Document Embeddings](https://aclanthology.org/2025.emnlp-main.1150/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
56. [Context-Efficient Retrieval with Factual Decomposition](https://aclanthology.org/2025.naacl-short.16/) *— 2025 · Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers)*
57. [Culturally-Nuanced Story Generation for Reasoning in Low-Resource Languages: The Case of Javanese and Sundanese](https://arxiv.org/abs/2502.12932) *— 2025 · arXiv:2502.12932*
58. [DTCRS: Dynamic Tree Construction for Recursive Summarization](https://aclanthology.org/2025.acl-long.536/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
59. [Demystify Verbosity Compensation Behavior of Large Language Models](https://aclanthology.org/2025.uncertainlp-main.14/) *— 2025 · Proceedings of the 2nd Workshop on Uncertainty-Aware NLP (UncertaiNLP 2025)*
60. [Distance between Relevant Information Pieces Causes Bias in Long-Context LLMs](https://aclanthology.org/2025.findings-acl.28/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
61. [DocBench: A Benchmark for Evaluating LLM-based Document Reading Systems](https://aclanthology.org/2025.knowledgenlp-1.29/) *— 2025 · Proceedings of the 4th International Workshop on Knowledge-Augmented Methods for Natural Language Processing*
62. [Dynamic Chunking and Selection for Reading Comprehension of Ultra-Long Context in Large Language Models](https://aclanthology.org/2025.acl-long.1538/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [DynamicKV: Task-Aware Adaptive KV Cache Compression for Long Context LLMs](https://aclanthology.org/2025.findings-emnlp.426/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
64. [E2LLM: Encoder Elongated Large Language Models for Long-Context Understanding and Reasoning](https://aclanthology.org/2025.emnlp-main.970/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
65. [ELITR-Bench: A Meeting Assistant Benchmark for Long-Context Language Models](https://aclanthology.org/2025.coling-main.28/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
66. [ETHIC: Evaluating Large Language Models on Long-Context Tasks with High Information Coverage](https://aclanthology.org/2025.naacl-long.283/) *— 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)*
67. [Efficient OpAmp Adaptation for Zoom Attention to Golden Contexts](https://aclanthology.org/2025.acl-long.653/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
68. [Evaluating the Effectiveness and Scalability of LLM-Based Data Augmentation for Retrieval](https://aclanthology.org/2025.emnlp-main.888/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
69. [EvolKV: Evolutionary KV Cache Compression for LLM Inference](https://aclanthology.org/2025.findings-emnlp.88/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
70. [FACT: Examining the Effectiveness of Iterative Context Rewriting for Multi-fact Retrieval](https://aclanthology.org/2025.findings-naacl.188/) *— 2025 · Findings of the Association for Computational Linguistics: NAACL 2025*
71. [FB-RAG: Improving RAG with Forward and Backward Lookup](https://aclanthology.org/2025.findings-ijcnlp.62/) *— 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*
72. [Facilitating Long Context Understanding via Supervised Chain-of-Thought Reasoning](https://aclanthology.org/2025.emnlp-main.615/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
73. [FarSense: A Comprehensive Commonsense Benchmark and Evaluation Framework for the Farsi Language](https://aclanthology.org/2025.ijcnlp-long.187/) *— 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*
74. [FicSim: A Dataset for Multi-Faceted Semantic Similarity in Long-Form Fiction](https://aclanthology.org/2025.findings-emnlp.1375/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
75. [Fine-Tuned LLMs are “Time Capsules” for Tracking Societal Bias Through Books](https://aclanthology.org/2025.naacl-long.118/) *— 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)*
76. [Flaw or Artifact? Rethinking Prompt Sensitivity in Evaluating LLMs](https://aclanthology.org/2025.emnlp-main.1006/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
77. [Glider: Global and Local Instruction-Driven Expert Router](https://aclanthology.org/2025.emnlp-main.319/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
78. [Guess What I am Thinking: A Benchmark for Inner Thought Reasoning of Role-Playing Language Agents](https://aclanthology.org/2025.findings-emnlp.819/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
79. [HSGM: Hierarchical Segment-Graph Memory for Scalable Long-Text Semantics](https://aclanthology.org/2025.starsem-1.26/) *— 2025 · Proceedings of the 14th Joint Conference on Lexical and Computational Semantics (*SEM 2025)*
80. [How to Train Long-Context Language Models (Effectively)](https://aclanthology.org/2025.acl-long.366/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
81. [Implicit Reasoning in Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2509.02350) *— 2025 · arXiv:2509.02350*
82. [Instella: Fully Open Language Models with Stellar Performance](https://arxiv.org/abs/2511.10628) *— 2025 · arXiv:2511.10628*
83. [Judge as A Judge: Improving the Evaluation of Retrieval-Augmented Generation through the Judge-Consistency of Large Language Models](https://aclanthology.org/2025.findings-acl.301/) *— 2025 · Findings of the Association for Computational Linguistics: ACL 2025*
84. [LADM: Long-context Training Data Selection with Attention-based Dependency Measurement for LLMs](https://aclanthology.org/2025.acl-long.154/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
85. [LLM-as-a-Judge Failures at Automating the Identification of Poor Quality Outputs in Free-Form Texts](https://aclanthology.org/2025.newsum-main.1/) *— 2025 · Proceedings of The 5th New Frontiers in Summarization Workshop*
86. [LLMs can be easily Confused by Instructional Distractions](https://aclanthology.org/2025.acl-long.957/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
87. [Large Language Models are good multi-lingual learners : When LLMs meet cross-lingual prompts](https://aclanthology.org/2025.coling-main.300/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
88. [Length-Induced Embedding Collapse in PLM-based Models](https://aclanthology.org/2025.acl-long.1396/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
89. [LiteraryQA: Towards Effective Evaluation of Long-document Narrative QA](https://aclanthology.org/2025.emnlp-main.1729/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
90. [LlmLink: Dual LLMs for Dynamic Entity Linking on Long Narratives with Collaborative Memorisation and Prompt Optimisation](https://aclanthology.org/2025.coling-main.751/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
91. [Long Chain-of-Thought Fine-tuning via Understanding-to-Reasoning Transition](https://aclanthology.org/2025.emnlp-main.1751/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
92. [LongLeader: A Comprehensive Leaderboard for Large Language Models in Long-context Scenarios](https://aclanthology.org/2025.naacl-long.439/) *— 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)*
93. [M-LongDoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework](https://aclanthology.org/2025.emnlp-main.469/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
94. [MDCure: A Scalable Pipeline for Multi-Document Instruction-Following](https://aclanthology.org/2025.acl-long.1418/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
95. [Morables: A Benchmark for Assessing Abstract Moral Reasoning in LLMs with Fables](https://aclanthology.org/2025.emnlp-main.1411/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
96. [More Tokens, Lower Precision: Towards the Optimal Token-Precision Trade-off in KV Cache Compression](https://aclanthology.org/2025.findings-emnlp.429/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
97. [More is not always better? Enhancing Many-Shot In-Context Learning with Differentiated and Reweighting Objectives](https://aclanthology.org/2025.acl-long.1475/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
98. [NarratEX Dataset: Explaining the Dominant Narratives in News Texts](https://aclanthology.org/2025.findings-emnlp.1112/) *— 2025 · Findings of the Association for Computational Linguistics: EMNLP 2025*
99. [NovelHopQA: Diagnosing Multi-Hop Reasoning Failures in Long Narrative Contexts](https://aclanthology.org/2025.emnlp-main.1328/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
100. [ONEBench to Test Them All: Sample-Level Benchmarking Over Open-Ended Capabilities](https://aclanthology.org/2025.acl-long.1560/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*

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