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# Who cites «Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences»

299 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/khashabi-2018-looking-beyond-surface-challenge-set/)

Pages: **1** · [2](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/khashabi-2018-looking-beyond-surface-challenge-set-2/) · [3](https://md.mova.elamur.ai/free/en/docs/papers/cited-by/khashabi-2018-looking-beyond-surface-challenge-set-3/)

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. [Charting the European LLM Benchmarking Landscape: A New Taxonomy and Registry](https://aclanthology.org/2026.llms4ssh-1.22/) *— 2026 · Proceedings of Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities (LLMs4SSH) @ LREC 2026*
3. [Decompose-and-Refine: Structured Legal Question Answering with Parametric Retrieval](https://arxiv.org/abs/2605.24454) *— 2026 · arXiv:2605.24454*
4. [High-Throughput and Memory-Efficient Zeroth-Order Fine-tuning LLMs with Distributed Parallel Computing](https://aclanthology.org/2026.findings-acl.2128/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
5. [Serbian SuperGLUE: Towards an Evaluation Benchmark for South Slavic Language Models](https://aclanthology.org/2026.loreslm-1.30/) *— 2026 · Proceedings of the Second Workshop on Language Models for Low-Resource Languages (LoResLM 2026)*
6. [Ultra-Low-Dimensional Prompt Tuning via Random Projection](https://aclanthology.org/2026.eacl-long.59/) *— 2026 · Proceedings of the 19th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
7. [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*
8. [ltzGLUE: Luxembourgish General Language Understanding Evaluation](https://aclanthology.org/2026.findings-acl.476/) *— 2026 · Findings of the Association for Computational Linguistics: ACL 2026*
9. [BiLD: Bi-directional Logits Difference Loss for Large Language Model Distillation](https://aclanthology.org/2025.coling-main.78/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
10. [CTCC: A Robust and Stealthy Fingerprinting Framework for Large Language Models via Cross-Turn Contextual Correlation Backdoor](https://aclanthology.org/2025.emnlp-main.356/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
11. [CausalEval: Towards Better Causal Reasoning in Language Models](https://aclanthology.org/2025.naacl-long.622/) *— 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)*
12. [Efficient and Effective Prompt Tuning via Prompt Decomposition and Compressed Outer Product](https://aclanthology.org/2025.naacl-long.225/) *— 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. [EverTracer: Hunting Stolen Large Language Models via Stealthy and Robust Probabilistic Fingerprint](https://aclanthology.org/2025.emnlp-main.358/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
14. [FPEdit: Robust LLM Fingerprinting through Localized Parameter Editing](https://arxiv.org/abs/2508.02092) *— 2025 · arXiv:2508.02092*
15. [Harmony in Divergence: Towards Fast, Accurate, and Memory-efficient Zeroth-order LLM Fine-tuning](https://arxiv.org/abs/2502.03304) *— 2025 · arXiv:2502.03304*
16. [Investigating Critical Period Effects in Language Acquisition through Neural Language Models](https://aclanthology.org/2025.tacl-1.5/) *— 2025 · Transactions of the Association for Computational Linguistics*
17. [MEraser: An Effective Fingerprint Erasure Approach for Large Language Models](https://aclanthology.org/2025.acl-long.1455/) *— 2025 · Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
18. [Machine Translation Meta Evaluation through Translation Accuracy Challenge Sets](https://aclanthology.org/2025.cl-1.4/) *— 2025 · Computational Linguistics*
19. [Once Upon a Time: Interactive Learning for Storytelling with Small Language Models](https://aclanthology.org/2025.babylm-main.32/) *— 2025 · Proceedings of the First BabyLM Workshop*
20. [Piecing It All Together: Verifying Multi-Hop Multimodal Claims](https://aclanthology.org/2025.coling-main.498/) *— 2025 · Proceedings of the 31st International Conference on Computational Linguistics*
21. [RecombiText: Compositional Data Augmentation for Enhancing LLM Pre-Training Datasets in Low-Resource Scenarios](https://aclanthology.org/2025.babylm-main.40/) *— 2025 · Proceedings of the First BabyLM Workshop*
22. [Self-Adjust Softmax](https://aclanthology.org/2025.emnlp-main.397/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
23. [Should I Share this Translation? Evaluating Quality Feedback for User Reliance on Machine Translation](https://aclanthology.org/2025.emnlp-main.606/) *— 2025 · Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing*
24. [Single layer tiny Co4 outpaces GPT-2 and GPT-BERT](https://aclanthology.org/2025.babylm-main.24/) *— 2025 · Proceedings of the First BabyLM Workshop*
25. [ZO2: Scalable Zeroth-Order Fine-Tuning for Extremely Large Language Models with Limited GPU Memory](https://arxiv.org/abs/2503.12668) *— 2025 · arXiv:2503.12668*
26. [A Multi-Task Embedder For Retrieval Augmented LLMs](https://aclanthology.org/2024.acl-long.194/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
27. [ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems](https://aclanthology.org/2024.naacl-long.20/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
28. [Addax: Utilizing Zeroth-Order Gradients to Improve Memory Efficiency and Performance of SGD for Fine-Tuning Language Models](https://arxiv.org/abs/2410.06441) *— 2024 · arXiv:2410.06441*
29. [Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model](https://arxiv.org/abs/2402.07827) *— 2024 · arXiv:2402.07827*
30. [Calibration-Tuning: Teaching Large Language Models to Know What They Don''t Know](https://aclanthology.org/2024.uncertainlp-1.1/) *— 2024 · Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024)*
31. [Continual Learning of Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2404.16789) *— 2024 · arXiv:2404.16789*
32. [Contrastive Instruction Tuning](https://aclanthology.org/2024.findings-acl.613/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
33. [Explaining and Improving Contrastive Decoding by Extrapolating the Probabilities of a Huge and Hypothetical LM](https://aclanthology.org/2024.emnlp-main.484/) *— 2024 · Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing*
34. [Full Parameter Fine-tuning for Large Language Models with Limited Resources](https://aclanthology.org/2024.acl-long.445/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
35. [GPT or BERT: why not both?](https://aclanthology.org/2024.conll-babylm.24/) *— 2024 · The 2nd BabyLM Challenge at the 28th Conference on Computational Natural Language Learning*
36. [In-context Learning with Retrieved Demonstrations for Language Models: A Survey](https://arxiv.org/abs/2401.11624) *— 2024 · arXiv:2401.11624*
37. [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*
38. [Instructional Fingerprinting of Large Language Models](https://aclanthology.org/2024.naacl-long.180/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
39. [Large Language Models Must Be Taught to Know What They Don't Know](https://arxiv.org/abs/2406.08391) *— 2024 · arXiv:2406.08391*
40. [Large Language Models: A Survey](https://arxiv.org/abs/2402.06196) *— 2024 · arXiv:2402.06196*
41. [Learning to Compress Prompt in Natural Language Formats](https://aclanthology.org/2024.naacl-long.429/) *— 2024 · Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers)*
42. [Learning to Retrieve In-Context Examples for Large Language Models](https://aclanthology.org/2024.eacl-long.105/) *— 2024 · Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)*
43. [Light-PEFT: Lightening Parameter-Efficient Fine-Tuning via Early Pruning](https://aclanthology.org/2024.findings-acl.447/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
44. [LoRAMoE: Alleviating World Knowledge Forgetting in Large Language Models via MoE-Style Plugin](https://aclanthology.org/2024.acl-long.106/) *— 2024 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
45. [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)*
46. [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*
47. [MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queries](https://arxiv.org/abs/2401.15391) *— 2024 · arXiv:2401.15391*
48. [On Evaluating Explanation Utility for Human-AI Decision Making in NLP](https://aclanthology.org/2024.findings-emnlp.439/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
49. [PEMT: Multi-Task Correlation Guided Mixture-of-Experts Enables Parameter-Efficient Transfer Learning](https://aclanthology.org/2024.findings-acl.410/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
50. [PORTULAN ExtraGLUE Datasets and Models: Kick-starting a Benchmark for the Neural Processing of Portuguese](https://aclanthology.org/2024.bucc-1.3/) *— 2024 · Proceedings of the 17th Workshop on Building and Using Comparable Corpora (BUCC) @ LREC-COLING 2024*
51. [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)*
52. [Plausible Extractive Rationalization through Semi-Supervised Entailment Signal](https://aclanthology.org/2024.findings-acl.307/) *— 2024 · Findings of the Association for Computational Linguistics: ACL 2024*
53. [Synthetic Multimodal Question Generation](https://aclanthology.org/2024.findings-emnlp.759/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
54. [Towards More Robust NLP System Evaluation: Handling Missing Scores in Benchmarks](https://aclanthology.org/2024.findings-emnlp.688/) *— 2024 · Findings of the Association for Computational Linguistics: EMNLP 2024*
55. [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)*
56. [tasksource: A Large Collection of NLP tasks with a Structured Dataset Preprocessing Framework](https://aclanthology.org/2024.lrec-main.1361/) *— 2024 · Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)*
57. [Étude des facteurs de complexité des modèles de langage dans une tâche de compréhension de lecture à l'aide d'une expérience contrôlée sémantiquement](https://aclanthology.org/2024.jeptalnrecital-taln.27/) *— 2024 · Actes de la 31ème Conférence sur le Traitement Automatique des Langues Naturelles, volume 1 : articles longs et prises de position*
58. [A Comprehensive Overview of Large Language Models](https://arxiv.org/abs/2307.06435) *— 2023 · arXiv:2307.06435*
59. [A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets](https://aclanthology.org/2023.findings-acl.29/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
60. [ACES: Translation Accuracy Challenge Sets at WMT 2023](https://aclanthology.org/2023.wmt-1.57/) *— 2023 · Proceedings of the Eighth Conference on Machine Translation*
61. [AD-KD: Attribution-Driven Knowledge Distillation for Language Model Compression](https://aclanthology.org/2023.acl-long.471/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [APrompt: Attention Prompt Tuning for Efficient Adaptation of Pre-trained Language Models](https://aclanthology.org/2023.emnlp-main.567/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
63. [ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems](https://arxiv.org/abs/2311.09476) *— 2023 · arXiv:2311.09476*
64. [Adapting Language Models to Compress Contexts](https://aclanthology.org/2023.emnlp-main.232/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
65. [Adapting Large Language Models to Domains via Reading Comprehension](https://arxiv.org/abs/2309.09530) *— 2023 · arXiv:2309.09530*
66. [Baby''s CoThought: Leveraging Large Language Models for Enhanced Reasoning in Compact Models](https://aclanthology.org/2023.conll-babylm.13/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
67. [Benchmarks for Automated Commonsense Reasoning: A Survey](https://arxiv.org/abs/2302.04752) *— 2023 · arXiv:2302.04752*
68. [BloombergGPT: A Large Language Model for Finance](https://arxiv.org/abs/2303.17564) *— 2023 · arXiv:2303.17564*
69. [ColD Fusion: Collaborative Descent for Distributed Multitask Finetuning](https://aclanthology.org/2023.acl-long.46/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
70. [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://arxiv.org/abs/2307.10172) *— 2023 · arXiv:2307.10172*
71. [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*
72. [Efficiently Adapting Pretrained Language Models To New Languages](https://arxiv.org/abs/2311.05741) *— 2023 · arXiv:2311.05741*
73. [Emergent Modularity in Pre-trained Transformers](https://aclanthology.org/2023.findings-acl.250/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
74. [Evaluating Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2310.19736) *— 2023 · arXiv:2310.19736*
75. [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)*
76. [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)*
77. [Fine-Tuning Language Models with Just Forward Passes](https://arxiv.org/abs/2305.17333) *— 2023 · arXiv:2305.17333*
78. [GlobalBench: A Benchmark for Global Progress in Natural Language Processing](https://aclanthology.org/2023.emnlp-main.875/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
79. [Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision](https://aclanthology.org/2023.emnlp-main.1001/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
80. [Incorporating Attribution Importance for Improving Faithfulness Metrics](https://aclanthology.org/2023.acl-long.261/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
81. [Knowledge is a Region in Weight Space for Fine-tuned Language Models](https://aclanthology.org/2023.findings-emnlp.95/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
82. [KoRC: Knowledge Oriented Reading Comprehension Benchmark for Deep Text Understanding](https://aclanthology.org/2023.findings-acl.743/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
83. [LAIT: Efficient Multi-Segment Encoding in Transformers with Layer-Adjustable Interaction](https://aclanthology.org/2023.acl-long.571/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
84. [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)*
85. [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)*
86. [Mean BERTs make erratic language teachers: the effectiveness of latent bootstrapping in low-resource settings](https://aclanthology.org/2023.conll-babylm.19/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
87. [Multi-CLS BERT: An Efficient Alternative to Traditional Ensembling](https://aclanthology.org/2023.acl-long.48/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
88. [Not all layers are equally as important: Every Layer Counts BERT](https://aclanthology.org/2023.conll-babylm.20/) *— 2023 · Proceedings of the BabyLM Challenge at the 27th Conference on Computational Natural Language Learning*
89. [PTP: Boosting Stability and Performance of Prompt Tuning with Perturbation-Based Regularizer](https://aclanthology.org/2023.emnlp-main.833/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
90. [Parameter Efficient Multi-task Fine-tuning by Learning to Transfer Token-wise Prompts](https://aclanthology.org/2023.findings-emnlp.584/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
91. [Parameter-efficient Tuning for Large Language Model without Calculating Its Gradients](https://aclanthology.org/2023.emnlp-main.22/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
92. [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*
93. [QUEST: A Retrieval Dataset of Entity-Seeking Queries with Implicit Set Operations](https://aclanthology.org/2023.acl-long.784/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
94. [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*
95. [Residual Prompt Tuning: improving prompt tuning with residual reparameterization](https://aclanthology.org/2023.findings-acl.421/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
96. [Revisiting Token Dropping Strategy in Efficient BERT Pretraining](https://aclanthology.org/2023.acl-long.579/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
97. [Symbolic Discovery of Optimization Algorithms](https://arxiv.org/abs/2302.06675) *— 2023 · arXiv:2302.06675*
98. [The Belebele Benchmark: a Parallel Reading Comprehension Dataset in 122 Language Variants](https://arxiv.org/abs/2308.16884) *— 2023 · arXiv:2308.16884 · Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics 749-775 2024*
99. [TimelineQA: A Benchmark for Question Answering over Timelines](https://aclanthology.org/2023.findings-acl.6/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
100. [Trained on 100 million words and still in shape: BERT meets British National Corpus](https://aclanthology.org/2023.findings-eacl.146/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*

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