---
title: "Allen Institute for Artificial Intelligence — works"
description: "169 works by researchers of Allen Institute for Artificial Intelligence in our library · page 1 of 2, the most cited first."
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---
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# Works of Allen Institute for Artificial Intelligence

169 works · page 1 of 2 · the most cited first · [← Allen Institute for Artificial Intelligence](https://md.research.elamur.ai/free/en/labs/allen-institute-for-artificial-intelligence/)

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1. [**SciBERT: A Pretrained Language Model for Scientific Text**Iz Beltagy, Kyle Lo, Arman Cohan*2019 · 3,548 citations*](https://md.research.elamur.ai/free/en/papers/beltagy-2019-scibert-pretrained-language-model-scientific/)
2. [**TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehension**Mandar Joshi, Eunsol Choi, Daniel Weld, et al.*2017 · 3,204 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2017-triviaqa-large-scale-distantly-supervised/)
3. [**SpanBERT: Improving Pre-training by Representing and Predicting Spans**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,580 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/)
4. [**VisualBERT: A Simple and Performant Baseline for Vision and Language**Liunian Harold Li, Mark Yatskar, Da Yin, et al.*2019 · 1,510 citations*](https://md.research.elamur.ai/free/en/papers/li-2019-visualbert-simple-performant-baseline-vision/)
5. [**Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?**Sewon Min, Xinxi Lyu, Ari Holtzman, et al.*2022 · 1,423 citations*](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/)
6. [**COMET: Commonsense Transformers for Automatic Knowledge Graph Construction**Antoine Bosselut, Hannah Rashkin, Maarten Sap, et al.*2019 · 1,282 citations*](https://md.research.elamur.ai/free/en/papers/bosselut-2019-comet-commonsense-transformers-automatic-knowledge/)
7. [**RealToxicityPrompts: Evaluating Neural Toxic Degeneration in Language Models**Samuel Gehman, Suchin Gururangan, Maarten Sap, et al.*2020 · 1,277 citations*](https://md.research.elamur.ai/free/en/papers/gehman-2020-realtoxicityprompts-evaluating-neural-toxic-degeneration/)
8. [**End-to-end Neural Coreference Resolution**Kenton Lee, Luheng He, Mike Lewis, et al.*2017 · 1,277 citations*](https://md.research.elamur.ai/free/en/papers/lee-2017-end-end-neural-coreference-resolution/)
9. [**PIQA: Reasoning about Physical Commonsense in Natural Language**Yonatan Bisk, Rowan Zellers, Ronan Le Bras, et al.*2019 · 1,218 citations*](https://md.research.elamur.ai/free/en/papers/bisk-2019-piqa-reasoning-about-physical-commonsense/)
10. [**Gender Bias in Coreference Resolution: Evaluation and Debiasing Methods**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2018 · 1,186 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2018-gender-bias-coreference-resolution-evaluation/)
11. [**The Risk of Racial Bias in Hate Speech Detection**Maarten Sap, Dallas Card, Saadia Gabriel, et al.*2019 · 1,148 citations*](https://md.research.elamur.ai/free/en/papers/sap-2019-risk-racial-bias-hate-speech/)
12. [**QuAC: Question Answering in Context**Eunsol Choi, He He, Mohit Iyyer, et al.*2018 · 1,057 citations*](https://md.research.elamur.ai/free/en/papers/choi-2018-quac-question-answering-context/)
13. [**Linguistic Knowledge and Transferability of Contextual Representations**Nelson F. Liu, Matt Gardner, Yonatan Belinkov, et al.*2019 · 1,011 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-linguistic-knowledge-transferability-contextual-representations/)
14. [**FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation**Sewon Min, Kalpesh Krishna, Xinxi Lyu, et al.*2023 · 970 citations*](https://md.research.elamur.ai/free/en/papers/min-2023-factscore-fine-grained-atomic-evaluation/)
15. [**Adversarial Example Generation with Syntactically Controlled Paraphrase Networks**Mohit Iyyer, John Wieting, Kevin Gimpel, et al.*2018 · 936 citations*](https://md.research.elamur.ai/free/en/papers/iyyer-2018-adversarial-example-generation-syntactically-controlled/)
16. [**Is Attention Interpretable?**Sofia Serrano, Noah A. Smith*2019 · 885 citations*](https://md.research.elamur.ai/free/en/papers/serrano-2019-is-attention-interpretable/)
17. [**UNIFIEDQA: Crossing Format Boundaries with a Single QA System**Daniel Khashabi, Sewon Min, Tushar Khot, et al.*2020 · 843 citations*](https://md.research.elamur.ai/free/en/papers/khashabi-2020-unifiedqa-crossing-format-boundaries-single/)
18. [**Entity, Relation, and Event Extraction with Contextualized Span Representations**David Wadden, Ulme Wennberg, Yi Luan, et al.*2019 · 818 citations*](https://md.research.elamur.ai/free/en/papers/wadden-2019-entity-relation-event-extraction-contextualized/)
19. [**Semi-supervised sequence tagging with bidirectional language models**Matthew E. Peters, Waleed Ammar, Chandra Bhagavatula, et al.*2017 · 786 citations*](https://md.research.elamur.ai/free/en/papers/peters-2017-semi-supervised-sequence-tagging-bidirectional/)
20. [**Towards Faithfully Interpretable NLP Systems: How Should We Define and Evaluate Faithfulness?**Alon Jacovi, Yoav Goldberg*2020 · 747 citations*](https://md.research.elamur.ai/free/en/papers/jacovi-2020-faithfully-interpretable-nlp-systems-how/)
21. [**ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks**Mohit Shridhar; Jesse Thomason; Daniel Gordon; Yonatan Bisk; Winson Han; Roozbeh Mottaghi; Luke Zettlemoyer;, Dieter Fox*2019 · 724 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2019-alfred-benchmark-interpreting-grounded-instructions/)
22. [**Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questions**Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, et al.*2022 · 711 citations*](https://md.research.elamur.ai/free/en/papers/trivedi-2022-interleaving-retrieval-chain-thought-reasoning/)
23. [**Fact or Fiction: Verifying Scientific Claims**David Wadden, Shanchuan Lin, Kyle Lo, et al.*2020 · 706 citations*](https://md.research.elamur.ai/free/en/papers/wadden-2020-fact-or-fiction-verifying-scientific/)
24. [**SPECTER: Document-level Representation Learning using Citation-informed Transformers**Arman Cohan, Sergey Feldman, Iz Beltagy, et al.*2020 · 690 citations*](https://md.research.elamur.ai/free/en/papers/cohan-2020-specter-document-level-representation-learning/)
25. [**CORD-19: The COVID-19 Open Research Dataset**Lucy Lu Wang, Kyle Lo, Yoganand Chandrasekhar, et al.*2020 · 685 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-cord-19-covid-19-open/)
26. [**Deep Semantic Role Labeling: What Works and What's Next**Luheng He, Kenton Lee, Mike Lewis, et al.*2017 · 589 citations*](https://md.research.elamur.ai/free/en/papers/he-2017-deep-semantic-role-labeling-what/)
27. [**Evaluating Gender Bias in Machine Translation**Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer*2019 · 588 citations*](https://md.research.elamur.ai/free/en/papers/stanovsky-2019-evaluating-gender-bias-machine-translation/)
28. [**Dissecting Contextual Word Embeddings: Architecture and Representation**Matthew E. Peters, Mark Neumann, Luke Zettlemoyer, et al.*2018 · 585 citations*](https://md.research.elamur.ai/free/en/papers/peters-2018-dissecting-contextual-word-embeddings-architecture/)
29. [**s1: Simple test-time scaling**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 584 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/)
30. [**ToxiGen: A Large-Scale Machine-Generated Dataset for Adversarial and Implicit Hate Speech Detection**Thomas Hartvigsen, Saadia Gabriel, Hamid Palangi, et al.*2022 · 571 citations*](https://md.research.elamur.ai/free/en/papers/hartvigsen-2022-toxigen-large-scale-machine-generated/)
31. [**Simple and Effective Multi-Paragraph Reading Comprehension**Christopher Clark, Matt Gardner*2017 · 556 citations*](https://md.research.elamur.ai/free/en/papers/clark-2017-simple-effective-multi-paragraph-reading/)
32. [**Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning**Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al.*2019 · 536 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/)
33. [**Learning to Solve Arithmetic Word Problems with Verb Categorization**Mohammad Javad Hosseini, Hannaneh Hajishirzi, Oren Etzioni, et al.*2014 · 513 citations*](https://md.research.elamur.ai/free/en/papers/hosseini-2014-learning-solve-arithmetic-word-problems/)
34. [**Crowdsourcing Multiple Choice Science Questions**Johannes Welbl, Nelson F. Liu, Matt Gardner*2017 · 513 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2017-crowdsourcing-multiple-choice-science-questions/)
35. [**Assessing BERT's Syntactic Abilities**Yoav Goldberg*2019 · 509 citations*](https://md.research.elamur.ai/free/en/papers/goldberg-2019-assessing-bert-s-syntactic-abilities/)
36. [**Gender Bias in Contextualized Word Embeddings**Jieyu Zhao, Tianlu Wang, Mark Yatskar, et al.*2019 · 503 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2019-gender-bias-contextualized-word-embeddings/)
37. [**A-OKVQA: A Benchmark for Visual Question Answering using World Knowledge**Dustin Schwenk, Apoorv Khandelwal, Christopher Clark, et al.*2022 · 490 citations*](https://md.research.elamur.ai/free/en/papers/schwenk-2022-okvqa-benchmark-visual-question-answering/)
38. [**BERT for Coreference Resolution: Baselines and Analysis**Mandar Joshi, Omer Levy, Luke Zettlemoyer, et al.*2019 · 488 citations*](https://md.research.elamur.ai/free/en/papers/joshi-2019-bert-coreference-resolution-baselines-analysis/)
39. [**Null It Out: Guarding Protected Attributes by Iterative Nullspace Projection**Shauli Ravfogel, Yanai Elazar, Hila Gonen, et al.*2020 · 480 citations*](https://md.research.elamur.ai/free/en/papers/ravfogel-2020-null-it-out-guarding-protected/)
40. [**Defending Against Neural Fake News**Rowan Zellers, Ari Holtzman, Hannah Rashkin, et al.*2019 · 461 citations*](https://md.research.elamur.ai/free/en/papers/zellers-2019-defending-against-neural-fake-news/)
41. [**Quality at a Glance: An Audit of Web-Crawled Multilingual Datasets**Julia Kreutzer, Isaac Caswell, Lisa Wang, et al.*2021 · 447 citations*](https://md.research.elamur.ai/free/en/papers/kreutzer-2021-quality-glance-audit-web-crawled/)
42. [**oLMpics — On what Language Model Pre-training Captures**Alon Talmor, Yanai Elazar, Yoav Goldberg, et al.*2019 · 432 citations*](https://md.research.elamur.ai/free/en/papers/talmor-2019-olmpics-language-model-pre-training/)
43. [**Deep Communicating Agents for Abstractive Summarization**Asli Celikyilmaz, Antoine Bosselut, Xiaodong He, et al.*2018 · 431 citations*](https://md.research.elamur.ai/free/en/papers/celikyilmaz-2018-deep-communicating-agents-abstractive-summarization/)
44. [**A general framework for information extraction using dynamic span graphs**Yi Luan, Dave Wadden, Luheng He, et al.*2019 · 427 citations*](https://md.research.elamur.ai/free/en/papers/luan-2019-general-framework-information-extraction-dynamic/)
45. [**Neural AMR: Sequence-to-Sequence Models for Parsing and Generation**Ioannis Konstas, Srinivasan Iyer, Mark Yatskar, et al.*2017 · 425 citations*](https://md.research.elamur.ai/free/en/papers/konstas-2017-neural-amr-sequence-sequence-models/)
46. [**UnifiedSKG: Unifying and Multi-Tasking Structured Knowledge Grounding with Text-to-Text Language Models**Tianbao Xie, Chen Henry Wu, Peng Shi, et al.*2022 · 405 citations*](https://md.research.elamur.ai/free/en/papers/xie-2022-unifiedskg-unifying-multi-tasking-structured/)
47. [**DExperts: Decoding-Time Controlled Text Generation with Experts and Anti-Experts**Alisa Liu, Maarten Sap, Ximing Lu, et al.*2021 · 399 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-dexperts-decoding-time-controlled-text/)
48. [**Construction of the Literature Graph in Semantic Scholar**Waleed Ammar, Dirk Groeneveld, Chandra Bhagavatula, et al.*2018 · 397 citations*](https://md.research.elamur.ai/free/en/papers/ammar-2018-construction-literature-graph-semantic-scholar/)
49. [**BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models**Elad Ben-Zaken, Shauli Ravfogel, Yoav Goldberg*2021 · 388 citations*](https://md.research.elamur.ai/free/en/papers/ben-zaken-2021-bitfit-simple-parameter-efficient-fine/)
50. [**♫ MuSiQue: Multihop Questions via Single-hop Question Composition**Harsh Trivedi, Niranjan Balasubramanian, Tushar Khot, et al.*2022 · 384 citations*](https://md.research.elamur.ai/free/en/papers/trivedi-2022-musique-multihop-questions-single-hop/)
51. [**Tulu 3: Pushing Frontiers in Open Language Model Post-Training**Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.*2024 · 379 citations*](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/)
52. [**Transformers as Soft Reasoners over Language**Peter Clark, Oyvind Tafjord, Kyle Richardson*2020 · 375 citations*](https://md.research.elamur.ai/free/en/papers/clark-2020-transformers-soft-reasoners-over-language/)
53. [**MRQA 2019 Shared Task: Evaluating Generalization in Reading Comprehension**Adam Fisch, Alon Talmor, Robin Jia, et al.*2019 · 375 citations*](https://md.research.elamur.ai/free/en/papers/fisch-2019-mrqa-2019-shared-task-evaluating/)
54. [**Editing Models with Task Arithmetic**Gabriel Ilharco, Marco Tulio Ribeiro, Mitchell Wortsman, et al.*2022 · 374 citations*](https://md.research.elamur.ai/free/en/papers/ilharco-2022-editing-models-task-arithmetic/)
55. [**Green AI**Roy Schwartz, Jesse Dodge, Noah A. Smith, et al.*2019 · 370 citations*](https://md.research.elamur.ai/free/en/papers/schwartz-2019-green-ai/)
56. [**Annotators with Attitudes: How Annotator Beliefs And Identities Bias Toxic Language Detection**Maarten Sap, Swabha Swayamdipta, Laura Vianna, et al.*2022 · 364 citations*](https://md.research.elamur.ai/free/en/papers/sap-2022-annotators-attitudes-how-annotator-beliefs/)
57. [**Adversarial Removal of Demographic Attributes from Text Data**Yanai Elazar, Yoav Goldberg*2018 · 357 citations*](https://md.research.elamur.ai/free/en/papers/elazar-2018-adversarial-removal-demographic-attributes-text/)
58. [**Do NLP Models Know Numbers? Probing Numeracy in Embeddings**Eric Wallace, Yizhong Wang, Sujian Li, et al.*2019 · 348 citations*](https://md.research.elamur.ai/free/en/papers/wallace-2019-do-nlp-models-know-numbers/)
59. [**Toxicity in chatgpt: Analyzing persona-assigned language models**Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, et al.*2023 · 344 citations*](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/)
60. [**Complexity-Based Prompting for Multi-Step Reasoning**Yao Fu, Hao Peng, Ashish Sabharwal, et al.*2022 · 343 citations*](https://md.research.elamur.ai/free/en/papers/fu-2022-complexity-based-prompting-multi-step/)
61. [**Symbolic Knowledge Distillation: from General Language Models to Commonsense Models**Peter West, Chandra Bhagavatula, Jack Hessel, et al.*2022 · 343 citations*](https://md.research.elamur.ai/free/en/papers/west-2022-symbolic-knowledge-distillation-general-language/)
62. [**Transformer Feed-Forward Layers Build Predictions by Promoting Concepts in the Vocabulary Space**Mor Geva, Avi Caciularu, Kevin Ro Wang, et al.*2022 · 342 citations*](https://md.research.elamur.ai/free/en/papers/geva-2022-transformer-feed-forward-layers-build/)
63. [**DREAM: A Challenge Data Set and Models for Dialogue-Based Reading Comprehension**Kai Sun, Dian Yu, Jianshu Chen, et al.*2019 · 331 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-dream-challenge-data-set-models/)
64. [**ProofWriter: Generating Implications, Proofs, and Abductive Statements over Natural Language**Oyvind Tafjord, Bhavana Dalvi, Peter Clark*2021 · 331 citations*](https://md.research.elamur.ai/free/en/papers/tafjord-2021-proofwriter-generating-implications-proofs-abductive/)
65. [**Unsupervised Domain Clusters in Pretrained Language Models**Roee Aharoni, Yoav Goldberg*2020 · 326 citations*](https://md.research.elamur.ai/free/en/papers/aharoni-2020-unsupervised-domain-clusters-pretrained-language/)
66. [**LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion**Dongfu Jiang, Xiang Ren, Bill Yuchen Lin*2023 · 314 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-llm-blender-ensembling-large-language/)
67. [**WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation**Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.*2022 · 312 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/)
68. [**Multi-hop Reading Comprehension through Question Decomposition and Rescoring**Sewon Min, Victor Zhong, Luke Zettlemoyer, et al.*2019 · 306 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-multi-hop-reading-comprehension-through/)
69. [**Abductive Commonsense Reasoning**Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, et al.*2019 · 301 citations*](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/)
70. [**Learning to Write with Cooperative Discriminators**Ari Holtzman, Jan Buys, Maxwell Forbes, et al.*2018 · 301 citations*](https://md.research.elamur.ai/free/en/papers/holtzman-2018-learning-write-cooperative-discriminators/)
71. [**S2ORC: The Semantic Scholar Open Research Corpus**Kyle Lo, Lucy Lu Wang, Mark Neumann, et al.*2019 · 297 citations*](https://md.research.elamur.ai/free/en/papers/lo-2019-s2orc-semantic-scholar-open-research/)
72. [**Structural Scaffolds for Citation Intent Classification in Scientific Publications**Arman Cohan, Waleed Ammar, Madeleine van Zuylen, et al.*2019 · 287 citations*](https://md.research.elamur.ai/free/en/papers/cohan-2019-structural-scaffolds-citation-intent-classification/)
73. [**Metaphor as a Medium for Emotion: An Empirical Study**Saif Mohammad, Ekaterina Shutova, Peter Turney*2016 · 285 citations*](https://md.research.elamur.ai/free/en/papers/mohammad-2016-metaphor-as-medium-emotion-empirical/)
74. [**A Dataset of Peer Reviews (PeerRead): Collection, Insights and NLP Applications**Dongyeop Kang, Waleed Ammar, Bhavana Dalvi, et al.*2018 · 274 citations*](https://md.research.elamur.ai/free/en/papers/kang-2018-dataset-peer-reviews-peerread-collection/)
75. [**Representing Schema Structure with Graph Neural Networks for Text-to-SQL Parsing**Ben Bogin, Jonathan Berant, Matt Gardner*2019 · 272 citations*](https://md.research.elamur.ai/free/en/papers/bogin-2019-representing-schema-structure-graph-neural/)
76. [**Surface Form Competition: Why the Highest Probability Answer Isn't Always Right**Ari Holtzman, Peter West, Vered Shwartz, et al.*2021 · 271 citations*](https://md.research.elamur.ai/free/en/papers/holtzman-2021-surface-form-competition-why-highest/)
77. [**Polyjuice: Generating Counterfactuals for Explaining, Evaluating, and Improving Models**Tongshuang Wu, Marco Tulio Ribeiro, Jeffrey Heer, et al.*2021 · 271 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-polyjuice-generating-counterfactuals-explaining-evaluating/)
78. [**Injecting Numerical Reasoning Skills into Language Models**Mor Geva, Ankit Gupta, Jonathan Berant*2020 · 268 citations*](https://md.research.elamur.ai/free/en/papers/geva-2020-injecting-numerical-reasoning-skills-into/)
79. [**TLDR: Extreme Summarization of Scientific Documents**Isabel Cachola, Kyle Lo, Arman Cohan, et al.*2020 · 260 citations*](https://md.research.elamur.ai/free/en/papers/cachola-2020-tldr-extreme-summarization-scientific-documents/)
80. [**Quoref: A Reading Comprehension Dataset with Questions Requiring Coreferential Reasoning**Pradeep Dasigi, Nelson F. Liu, Ana Marasovic, et al.*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2019-quoref-reading-comprehension-dataset-questions/)
81. [**The Multilingual Amazon Reviews Corpus**Phillip Keung, Yichao Lu, György Szarvas, et al.*2020 · 255 citations*](https://md.research.elamur.ai/free/en/papers/keung-2020-multilingual-amazon-reviews-corpus/)
82. [**Neural Cross-Lingual Named Entity Recognition with Minimal Resources**Jiateng Xie, Zhilin Yang, Graham Neubig, et al.*2018 · 247 citations*](https://md.research.elamur.ai/free/en/papers/xie-2018-neural-cross-lingual-named-entity/)
83. [**MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers**Krishna Pillutla, Swabha Swayamdipta, Rowan Zellers, et al.*2021 · 243 citations*](https://md.research.elamur.ai/free/en/papers/pillutla-2021-mauve-measuring-gap-between-neural/)
84. [**Amnesic Probing: Behavioral Explanation with Amnesic Counterfactuals**Yanai Elazar, Shauli Ravfogel, Alon Jacovi, et al.*2021 · 241 citations*](https://md.research.elamur.ai/free/en/papers/elazar-2021-amnesic-probing-behavioral-explanation-amnesic/)
85. [**Noisy Channel Language Model Prompting for Few-Shot Text Classification**Sewon Min, Mike Lewis, Hannaneh Hajishirzi, et al.*2021 · 238 citations*](https://md.research.elamur.ai/free/en/papers/min-2021-noisy-channel-language-model-prompting/)
86. [**Reframing Instructional Prompts to GPTk's Language**Swaroop Mishra, Daniel Khashabi, Chitta Baral, et al.*2022 · 236 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2022-reframing-instructional-prompts-gptk-s/)
87. [**COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs**Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, et al.*2020 · 235 citations*](https://md.research.elamur.ai/free/en/papers/hwang-2020-comet-atomic-2020-symbolic-neural/)
88. [**MultiQA: An Empirical Investigation of Generalization and Transfer in Reading Comprehension**Alon Talmor, Jonathan Berant*2019 · 234 citations*](https://md.research.elamur.ai/free/en/papers/talmor-2019-multiqa-empirical-investigation-generalization-transfer/)
89. [**Evaluating Open-Domain Question Answering in the Era of Large Language Models**Ehsan Kamalloo, Nouha Dziri, Charles Clarke, et al.*2023 · 233 citations*](https://md.research.elamur.ai/free/en/papers/kamalloo-2023-evaluating-open-domain-question-answering/)
90. [**A Discrete Hard EM Approach for Weakly Supervised Question Answering**Sewon Min, Danqi Chen, Hannaneh Hajishirzi, et al.*2019 · 233 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-discrete-hard-em-approach-weakly/)
91. [**Compositional Questions Do Not Necessitate Multi-hop Reasoning**Sewon Min, Eric Wallace, Sameer Singh, et al.*2019 · 231 citations*](https://md.research.elamur.ai/free/en/papers/min-2019-compositional-questions-do-not-necessitate/)
92. [**Specializing Smaller Language Models towards Multi-Step Reasoning**Yao Fu, Hao Peng, Litu Ou, et al.*2023 · 222 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-specializing-smaller-language-models-towards/)
93. [**How Language Model Hallucinations Can Snowball**Muru Zhang, Ofir Press, William Merrill, et al.*2023 · 221 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-language-model-hallucinations-snowball/)
94. [**Prometheus 2: An Open Source Language Model Specialized in Evaluating Other Language Models**Seungone Kim, Juyoung Suk, Shayne Longpre, et al.*2024 · 220 citations*](https://md.research.elamur.ai/free/en/papers/kim-2024-prometheus-2-open-source-language/)
95. [**Break It Down: A Question Understanding Benchmark**Tomer Wolfson, Mor Geva, Ankit Gupta, et al.*2020 · 218 citations*](https://md.research.elamur.ai/free/en/papers/wolfson-2020-break-it-down-question-understanding/)
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