🧑‍🔬 Who cites Sandro Pezzelle

681 citations of their works from 675 papers of our library (of the 141,840 whose bibliographies we read) · 79 with a page here, the most cited first · ← Sandro Pezzelle

1. Language Models are Few-Shot LearnersarXiv:2005.14165Tom B. Brown, Benjamin Mann, Nick Ryder, et al.2020 · 13,703 citations
2. PaLM: Scaling Language Modeling with PathwaysarXiv:2204.02311Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.2022 · 4,680 citations
3. Natural Questions: A Benchmark for Question Answering ResearchTransactions of the Association for Computational Linguistics (TACL)Tom Kwiatkowski, Jennimaria Palomaki, Olivia Redfield, et al.2019 · 4,035 citations
4. TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading ComprehensionAnnual Meeting of the Association for Computational Linguistics (ACL)Mandar Joshi, Eunsol Choi, Daniel Weld, et al.2017 · 3,213 citations
5. On the Opportunities and Risks of Foundation ModelsarXiv:2108.07258Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.2021 · 3,056 citations
6. Gemini: A Family of Highly Capable Multimodal ModelsarXiv:2312.11805Gemini Team, Rohan Anil, Sebastian Borgeaud, et al.2023 · 2,957 citations
7. A Survey of Large Language ModelsFrontiers of Computer ScienceWayne Xin Zhao, Kun Zhou, Junyi Li, et al.2023 · 2,863 citations
8. ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language TasksarXiv:1908.02265Jiasen Lu, Dhruv Batra, Devi Parikh, et al.2019 · 2,237 citations
9. Training Compute-Optimal Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.2022 · 1,950 citations
10. Adversarial Examples for Evaluating Reading Comprehension SystemsRobin Jia, Percy Liang2017 · 1,942 citations
11. Qwen Technical ReportarXiv:2309.16609Jinze Bai, Shuai Bai, Yunfei Chu, et al.2023 · 1,645 citations
12. Multitask Prompted Training Enables Zero-Shot Task GeneralizationarXiv:2110.08207Victor Sanh, Albert Webson, Colin Raffel, et al.2021 · 1,581 citations
13. CLIPScore: A Reference-free Evaluation Metric for Image CaptioningConference on Empirical Methods in Natural Language Processing (EMNLP)Jack Hessel, Ari Holtzman, Maxwell Forbes, et al.2021 · 1,499 citations
14. GLM: General Language Model Pretraining with Autoregressive Blank InfillingAnnual Meeting of the Association for Computational Linguistics (ACL)Zhengxiao Du, Yujie Qian, Xiao Liu, et al.2022 · 1,452 citations
15. A Comprehensive Overview of Large Language ModelsACM Transactions on Intelligent Systems and TechnologyHumza Naveed, Asad Ullah Khan, Shi Qiu, et al.2023 · 1,286 citations
16. What you can cram into a single $&!#* vector: Probing sentence embeddings for linguistic propertiesAnnual Meeting of the Association for Computational Linguistics (ACL)Alexis Conneau, German Kruszewski, Guillaume Lample, et al.2018 · 1,026 citations
17. PaLM 2 Technical ReportarXiv:2305.10403Rohan Anil, Andrew M. Dai, Orhan Firat, et al.2023 · 977 citations
18. DROP: A Reading Comprehension Benchmark Requiring Discrete Reasoning Over ParagraphsDheeru Dua, Yizhong Wang, Pradeep Dasigi, et al.2019 · 943 citations
19. GPT-NeoX-20B: An Open-Source Autoregressive Language ModelBigScience Episode – Workshop on Challenges & Perspectives in Creating Large Language ModelsSidney Black, Stella Biderman, Eric Hallahan, et al.2022 · 821 citations
20. GLM-130B: An Open Bilingual Pre-trained ModelarXiv:2210.02414Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.2022 · 779 citations
21. Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language ModelarXiv:2201.11990Shaden Smith, Mostofa Patwary, Brandon Norick, et al.2022 · 602 citations
22. AGIEval: A Human-Centric Benchmark for Evaluating Foundation ModelsFindings of the Association for Computational LinguisticsWanjun Zhong, Ruixiang Cui, Yiduo Guo, et al.2024 · 564 citations
23. Evaluating Models' Local Decision Boundaries via Contrast SetsMatt Gardner, Yoav Artzi, Victoria Basmov, et al.2020 · 522 citations
24. Crowdsourcing Multiple Choice Science QuestionsWorkshop on Noisy User-generated TextJohannes Welbl, Nelson F. Liu, Matt Gardner2017 · 516 citations
25. GLaM: Efficient Scaling of Language Models with Mixture-of-ExpertsarXiv:2112.06905Nan Du, Yanping Huang, Andrew M. Dai, et al.2021 · 479 citations
26. The Falcon Series of Open Language ModelsarXiv:2311.16867Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.2023 · 436 citations
27. Experience Grounds LanguageConference on Empirical Methods in Natural Language Processing (EMNLP)Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.2020 · 430 citations
28. FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPUarXiv:2303.06865Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.2023 · 399 citations
29. Long Range Arena: A Benchmark for Efficient TransformersarXiv:2011.04006Yi Tay, Mostafa Dehghani, Samira Abnar, et al.2020 · 399 citations
30. A Survey on Model Compression for Large Language ModelsTransactions of the Association for Computational Linguistics (TACL)Xunyu Zhu, Jian Li, Yong Liu, et al.2024 · 381 citations
31. ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading ComprehensionarXiv:1810.12885Sheng Zhang, Xiaodong Liu, Jingjing Liu, et al.2018 · 381 citations
32. How Much Reading Does Reading Comprehension Require? A Critical Investigation of Popular BenchmarksDivyansh Kaushik, Zachary C. Lipton2018 · 361 citations
33. Compressive Transformers for Long-Range Sequence ModellingarXiv:1911.05507Jack W. Rae, Anna Potapenko, Siddhant M. Jayakumar, et al.2019 · 326 citations
34. ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale TransformersConference on Neural Information Processing Systems (NeurIPS)Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.2022 · 302 citations
35. An Explanation of In-context Learning as Implicit Bayesian InferencearXiv:2111.02080Sang Michael Xie, Aditi Raghunathan, Percy Liang, et al.2021 · 269 citations
36. DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI ScalearXiv:2201.05596Samyam Rajbhandari, Conglong Li, Zhewei Yao, et al.2022 · 255 citations
37. MiniF2F: a cross-system benchmark for formal Olympiad-level mathematicsarXiv:2109.00110Kunhao Zheng, Jesse Michael Han, Stanislas Polu2021 · 255 citations
38. Personality Traits in Large Language ModelsarXiv:2307.00184Greg Serapio-Garcia, Mustafa Safdari, Clement Crepy, et al.2023 · 251 citations
39. RedPajama: an Open Dataset for Training Large Language ModelsConference on Neural Information Processing Systems (NeurIPS)Maurice Weber, Daniel Fu, Quentin Anthony, et al.2024 · 247 citations
40. Aya Model: An Instruction Finetuned Open-Access Multilingual Language ModelarXiv:2402.07827Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.2024 · 245 citations
41. Coreferential Reasoning Learning for Language RepresentationConference on Empirical Methods in Natural Language Processing (EMNLP)Deming Ye, Yankai Lin, Jiaju Du, et al.2020 · 225 citations
42. Challenges in Detoxifying Language ModelsFindings of the Association for Computational LinguisticsJohannes Welbl, Amelia Glaese, Jonathan Uesato, et al.2021 · 207 citations
43. Aya Dataset: An Open-Access Collection for Multilingual Instruction TuningAnnual Meeting of the Association for Computational Linguistics (ACL)Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.2024 · 203 citations
44. TinyStories: How Small Can Language Models Be and Still Speak Coherent English?Ronen Eldan, Yuanzhi Li2023 · 202 citations
45. LLMs-as-Judges: A Comprehensive Survey on LLM-based Evaluation MethodsarXiv:2412.05579Haitao Li, Qian Dong, Junjie Chen, et al.2024 · 189 citations
46. An Information-theoretic Approach to Prompt Engineering Without Ground Truth LabelsAnnual Meeting of the Association for Computational Linguistics (ACL)Taylor Sorensen, Joshua Robinson, Christopher Rytting, et al.2022 · 178 citations
47. What Makes Reading Comprehension Questions Easier?Conference on Empirical Methods in Natural Language Processing (EMNLP)Saku Sugawara, Kentaro Inui, Satoshi Sekine, et al.2018 · 178 citations
48. Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-SpecificityarXiv:2310.07521Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.2023 · 170 citations
49. PanGu-α: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel ComputationarXiv:2104.12369Wei Zeng, Xiaozhe Ren, Teng Su, et al.2021 · 161 citations
50. Orca 2: Teaching Small Language Models How to ReasonarXiv:2311.11045Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.2023 · 160 citations
51. DataComp-LM: In search of the next generation of training sets for language modelsConference on Neural Information Processing Systems (NeurIPS)Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.2024 · 156 citations
52. Neural Models for Reasoning over Multiple Mentions Using CoreferenceConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Bhuwan Dhingra, Qiao Jin, Zhilin Yang, et al.2018 · 147 citations
53. Efficient Training of Language Models to Fill in the MiddlearXiv:2207.14255Mohammad Bavarian, Heewoo Jun, Nikolas Tezak, et al.2022 · 138 citations
54. Simple and Effective Masked Diffusion Language ModelsConference on Neural Information Processing Systems (NeurIPS)Subham Sekhar Sahoo, Marianne Arriola, Yair Schiff, et al.2024 · 134 citations
55. Recent Advances in Natural Language Inference: A Survey of Benchmarks, Resources, and ApproachesarXiv:1904.01172Shane Storks, Qiaozi Gao, Joyce Y. Chai2019 · 134 citations
56. Knowledge Unlearning for Mitigating Privacy Risks in Language ModelsarXiv:2210.01504Joel Jang, Dongkeun Yoon, Sohee Yang, et al.2022 · 126 citations
57. The case for 4-bit precision: k-bit Inference Scaling LawsarXiv:2212.09720Tim Dettmers, Luke Zettlemoyer2022 · 119 citations
58. ChID: A Large-scale Chinese IDiom Dataset for Cloze TestAnnual Meeting of the Association for Computational Linguistics (ACL)Chujie Zheng, Minlie Huang, Aixin Sun2019 · 119 citations
59. Pretraining Language Models with Human PreferencesarXiv:2302.08582Tomasz Korbak, Kejian Shi, Angelica Chen, et al.2023 · 118 citations
60. Olmo 3arXiv:2512.13961Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.2025 · 109 citations
61. Tokenizer Choice For LLM Training: Negligible or Crucial?Findings of the Association for Computational LinguisticsMehdi Ali, Michael Fromm, Klaudia Thellmann, et al.2024 · 102 citations
62. Inverse Scaling: When Bigger Isn't BetterarXiv:2306.09479 · Transactions on Machine Learning Research (TMLR), 10/2023, https://openreview.net/forum?id=DwgRm72GQFIan R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.2023 · 99 citations
63. LLaMA-MoE: Building Mixture-of-Experts from LLaMA with Continual Pre-TrainingConference on Empirical Methods in Natural Language Processing (EMNLP)Tong Zhu, Xiaoye Qu, Daize Dong, et al.2024 · 85 citations
64. PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics CapabilitiesSettaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.2024 · 75 citations
65. English Machine Reading Comprehension Datasets: A SurveyDaria Dzendzik, Jennifer Foster, Carl Vogel2021 · 54 citations
66. Scaling Laws of RoPE-based ExtrapolationarXiv:2310.05209Xiaoran Liu, Hang Yan, Shuo Zhang, et al.2023 · 53 citations
67. Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling LawsarXiv:2401.00448Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.2024 · 51 citations
68. Transcending Scaling Laws with 0.1% Extra ComputearXiv:2210.11399Yi Tay, Jason Wei, Hyung Won Chung, et al.2022 · 41 citations
69. Distillation Scaling LawsarXiv:2502.08606Dan Busbridge, Amitis Shidani, Floris Weers, et al.2025 · 23 citations
70. Advancing Dialectal Arabic to Modern Standard Arabic Machine TranslationarXiv:2507.20301Abdullah Alabdullah, Lifeng Han, Chenghua Lin2025 · 8 citations
71. Multi-modal, Multi-task, Multi-criteria Automatic Evaluation with Vision Language ModelsLanguage Resources and Evaluation Conference (LREC)Masanari Oi, Masahiro Kaneko, Naoaki Okazaki, et al.2026 · 1 citation
72. Sense and Sensitivity: “Reasoning” Models are More Robust, but can Diverge from Human Consensus in a Legal Interpretation TaskConference on Computational Natural Language Learning (CoNLL)Dawson Petersen, Abhishek Purushothama, Nathan Schneider2026 · 1 citation
73. Child-directed speech facilitates production, not comprehension, in BabyLMsConference on Computational Natural Language Learning (CoNLL)Bastian Bunzeck, Sina Zarrieß2026
74. Evaluation of Document-Level Text Simplification in JapaneseLanguage Resources and Evaluation Conference (LREC)Iori Yamashita, Hikari Tanaka, Hajime Kiyama, et al.2026
75. GlossMATE: Multi-Agent Translator Explanations for GlossesLanguage Resources and Evaluation Conference (LREC)Changbing Yang, Patrick Littell, Gabriel Bernier-Colborne, et al.2026
76. Judging Instruction Responses in a Low-Resource Language: A Case Study on BasqueLanguage Resources and Evaluation Conference (LREC)David Ponce, Harritxu Gete, Thierry Etchegoyhen, et al.2026
77. Mechanistic Interpretability Meets Cognitive Linguistics: Modelling Locative Image Schemas in the Circuit FrameworkLanguage Resources and Evaluation Conference (LREC)Mattia Proietti, Afra Alishahi, Grzegorz Chrupała, et al.2026
78. Quantifying the Impact of Translation Errors on Multilingual LLM EvaluationAnnual Meeting of the Association for Computational Linguistics (ACL)Klaudia Thellmann, Bernhard Stadler, Michael Färber, et al.2026
79. Rethinking Evaluation in Retrieval-Augmented Personalized Dialogue: A Cognitive and Linguistic PerspectiveLanguage Resources and Evaluation Conference (LREC)Tianyi Zhang, David Traum2026

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