---
title: "Who cites Looking Beyond the Surface: A Challenge Set for…"
description: "The papers of the Elamur Research library that cite «Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences», found…"
canonical: "https://research.elamur.ai/free/en/papers/khashabi-2018-looking-beyond-surface-challenge-set/cited-by/page/2/"
---
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# Who cites «Looking Beyond the Surface: A Challenge Set for Reading Comprehension over Multiple Sentences» · page 2

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

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

1. [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*
2. [ARES: An Automated Evaluation Framework for Retrieval-Augmented Generation Systems](https://arxiv.org/abs/2311.09476) *— 2023 · arXiv:2311.09476*
3. [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*
4. [Adapting Large Language Models to Domains via Reading Comprehension](https://arxiv.org/abs/2309.09530) *— 2023 · arXiv:2309.09530*
5. [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*
6. [Bayesian Multi-Task Transfer Learning for Soft Prompt Tuning](https://aclanthology.org/2023.findings-emnlp.329/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
7. [Benchmarks for Automated Commonsense Reasoning: A Survey](https://arxiv.org/abs/2302.04752) *— 2023 · arXiv:2302.04752*
8. [BloombergGPT: A Large Language Model for Finance](https://arxiv.org/abs/2303.17564) *— 2023 · arXiv:2303.17564*
9. [Catwalk: A Unified Language Model Evaluation Framework for Many Datasets](https://arxiv.org/abs/2312.10253) *— 2023 · arXiv:2312.10253*
10. [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)*
11. [DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AI](https://arxiv.org/abs/2307.10172) *— 2023 · arXiv:2307.10172*
12. [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*
13. [Efficiently Adapting Pretrained Language Models To New Languages](https://arxiv.org/abs/2311.05741) *— 2023 · arXiv:2311.05741*
14. [Emergent Modularity in Pre-trained Transformers](https://aclanthology.org/2023.findings-acl.250/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
15. [Entity Tracking via Effective Use of Multi-Task Learning Model and Mention-guided Decoding](https://aclanthology.org/2023.eacl-main.90/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
16. [Evaluating Large Language Models: A Comprehensive Survey](https://arxiv.org/abs/2310.19736) *— 2023 · arXiv:2310.19736*
17. [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)*
18. [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)*
19. [Fine-Tuning Language Models with Just Forward Passes](https://arxiv.org/abs/2305.17333) *— 2023 · arXiv:2305.17333*
20. [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*
21. [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*
22. [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)*
23. [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*
24. [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*
25. [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)*
26. [Learning Easily Updated General Purpose Text Representations with Adaptable Task-Specific Prefix](https://aclanthology.org/2023.findings-emnlp.497/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
27. [Learning to Ignore Adversarial Attacks](https://aclanthology.org/2023.eacl-main.216/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
28. [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)*
29. [Learning to Predict Task Transferability via Soft Prompt](https://aclanthology.org/2023.emnlp-main.546/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
30. [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)*
31. [Make Your Decision Convincing! A Unified Two-Stage Framework: Self-Attribution and Decision-Making](https://aclanthology.org/2023.findings-emnlp.78/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
32. [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*
33. [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)*
34. [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*
35. [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*
36. [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*
37. [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*
38. [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*
39. [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)*
40. [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*
41. [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*
42. [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)*
43. [SharPT: Shared Latent Space Prompt Tuning](https://aclanthology.org/2023.findings-eacl.92/) *— 2023 · Findings of the Association for Computational Linguistics: EACL 2023*
44. [Symbolic Discovery of Optimization Algorithms](https://arxiv.org/abs/2302.06675) *— 2023 · arXiv:2302.06675*
45. [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*
46. [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*
47. [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*
48. [Two Examples are Better than One: Context Regularization for Gradient-based Prompt Tuning](https://aclanthology.org/2023.findings-acl.206/) *— 2023 · Findings of the Association for Computational Linguistics: ACL 2023*
49. [UPRISE: Universal Prompt Retrieval for Improving Zero-Shot Evaluation](https://aclanthology.org/2023.emnlp-main.758/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
50. [UltraFeedback: Boosting Language Models with Scaled AI Feedback](https://arxiv.org/abs/2310.01377) *— 2023 · arXiv:2310.01377*
51. [Understanding the Role of Input Token Characters in Language Models: How Does Information Loss Affect Performance?](https://aclanthology.org/2023.emnlp-main.563/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
52. [Unsupervised Selective Rationalization with Noise Injection](https://aclanthology.org/2023.acl-long.707/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
53. [Vote”n”Rank: Revision of Benchmarking with Social Choice Theory](https://aclanthology.org/2023.eacl-main.48/) *— 2023 · Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics*
54. [Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision](https://arxiv.org/abs/2312.09390) *— 2023 · arXiv:2312.09390*
55. [What's the Meaning of Superhuman Performance in Today's NLU?](https://aclanthology.org/2023.acl-long.697/) *— 2023 · Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
56. [When Your Language Model Cannot Even Do Determiners Right: Probing for Anti-Presuppositions and the Maximize Presupposition! Principle](https://md.research.elamur.ai/free/en/papers/sieker-2023-when-your-language-model-cannot/) *— our card · 2023 · Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP*
57. [Where to start? Analyzing the potential value of intermediate models](https://aclanthology.org/2023.emnlp-main.90/) *— 2023 · Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing*
58. [kNN-CM: A Non-parametric Inference-Phase Adaptation of Parametric Text Classifiers](https://aclanthology.org/2023.findings-emnlp.903/) *— 2023 · Findings of the Association for Computational Linguistics: EMNLP 2023*
59. [ACES: Translation Accuracy Challenge Sets for Evaluating Machine Translation Metrics](https://md.research.elamur.ai/free/en/papers/amrhein-2022-aces-translation-accuracy-challenge-sets/) *— our card · 2022 · Proceedings of the Seventh Conference on Machine Translation (WMT)*
60. [ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts](https://aclanthology.org/2022.emnlp-main.446/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
61. [AdapLeR: Speeding up Inference by Adaptive Length Reduction](https://aclanthology.org/2022.acl-long.1/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
62. [An Investigation of the (In)effectiveness of Counterfactually Augmented Data](https://aclanthology.org/2022.acl-long.256/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
63. [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)*
64. [Ask Me Anything: A simple strategy for prompting language models](https://arxiv.org/abs/2210.02441) *— 2022 · arXiv:2210.02441*
65. [Automatic Label Sequence Generation for Prompting Sequence-to-sequence Models](https://aclanthology.org/2022.coling-1.440/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
66. [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*
67. [Can Edge Probing Tests Reveal Linguistic Knowledge in QA Models?](https://aclanthology.org/2022.coling-1.139/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
68. [Can Rationalization Improve Robustness?](https://aclanthology.org/2022.naacl-main.278/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
69. [Coarse-to-Fine: Hierarchical Multi-task Learning for Natural Language Understanding](https://aclanthology.org/2022.coling-1.439/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
70. [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)*
71. [Design Challenges for a Multi-Perspective Search Engine](https://aclanthology.org/2022.findings-naacl.22/) *— 2022 · Findings of the Association for Computational Linguistics: NAACL 2022*
72. [Double Trouble: How to not Explain a Text Classifier”s Decisions Using Counterfactuals Synthesized by Masked Language Models?](https://aclanthology.org/2022.aacl-main.2/) *— 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)*
73. [Down and Across: Introducing Crossword-Solving as a New NLP Benchmark](https://aclanthology.org/2022.acl-long.189/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
74. [Eliciting and Understanding Cross-task Skills with Task-level Mixture-of-Experts](https://aclanthology.org/2022.findings-emnlp.189/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
75. [FewNLU: Benchmarking State-of-the-Art Methods for Few-Shot Natural Language Understanding](https://aclanthology.org/2022.acl-long.38/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
76. [FlipDA: Effective and Robust Data Augmentation for Few-Shot Learning](https://aclanthology.org/2022.acl-long.592/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
77. [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*
78. [Human-Centered Evaluation of Explanations](https://aclanthology.org/2022.naacl-tutorials.4/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies: Tutorial Abstracts*
79. [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*
80. [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*
81. [Learning to Perform Complex Tasks through Compositional Fine-Tuning of Language Models](https://aclanthology.org/2022.findings-emnlp.121/) *— 2022 · Findings of the Association for Computational Linguistics: EMNLP 2022*
82. [Lite Unified Modeling for Discriminative Reading Comprehension](https://aclanthology.org/2022.acl-long.594/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
83. [Locate Then Ask: Interpretable Stepwise Reasoning for Multi-hop Question Answering](https://aclanthology.org/2022.coling-1.142/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
84. [On the Diversity and Limits of Human Explanations](https://aclanthology.org/2022.naacl-main.158/) *— 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies*
85. [Reframing Instructional Prompts to GPTk's Language](https://aclanthology.org/2022.findings-acl.50/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
86. [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)*
87. [Towards a Unified Multi-Dimensional Evaluator for Text Generation](https://aclanthology.org/2022.emnlp-main.131/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
88. [Transferring Confluent Knowledge to Argument Mining](https://aclanthology.org/2022.coling-1.597/) *— 2022 · Proceedings of the 29th International Conference on Computational Linguistics*
89. [UKP-SQUARE: An Online Platform for Question Answering Research](https://aclanthology.org/2022.acl-demo.2/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: System Demonstrations*
90. [UNIREX: A Unified Learning Framework for Language Model Rationale Extraction](https://aclanthology.org/2022.bigscience-1.5/) *— 2022 · Proceedings of BigScience Episode \#5 – Workshop on Challenges & Perspectives in Creating Large Language Models*
91. [Vector-Quantized Input-Contextualized Soft Prompts for Natural Language Understanding](https://aclanthology.org/2022.emnlp-main.455/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
92. [What Language Model Architecture and Pretraining Objective Work Best for Zero-Shot Generalization?](https://arxiv.org/abs/2204.05832) *— 2022 · arXiv:2204.05832*
93. [What Language Model to Train if You Have One Million GPU Hours?](https://arxiv.org/abs/2210.15424) *— 2022 · arXiv:2210.15424*
94. [What Makes Reading Comprehension Questions Difficult?](https://aclanthology.org/2022.acl-long.479/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
95. [What to Learn, and How: Toward Effective Learning from Rationales](https://aclanthology.org/2022.findings-acl.86/) *— 2022 · Findings of the Association for Computational Linguistics: ACL 2022*
96. [Word Order Does Matter and Shuffled Language Models Know It](https://aclanthology.org/2022.acl-long.476/) *— 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)*
97. [XPrompt: Exploring the Extreme of Prompt Tuning](https://aclanthology.org/2022.emnlp-main.758/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
98. [Zero-Shot Learners for Natural Language Understanding via a Unified Multiple Choice Perspective](https://aclanthology.org/2022.emnlp-main.474/) *— 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing*
99. [An Investigation of Language Model Interpretability via Sentence Editing](https://aclanthology.org/2021.blackboxnlp-1.34/) *— 2021 · Proceedings of the Fourth BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP*
100. [Benchmarking Machine Reading Comprehension: A Psychological Perspective](https://aclanthology.org/2021.eacl-main.137/) *— 2021 · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume*

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