---
title: "Who cites Hector J. Levesque — 757 papers of our library"
description: "757 papers of the Elamur Research library cite the works of Hector J. Levesque, found in their bibliographies · page 1 of 3 · 1–100; the most…"
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---
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# Who cites Hector J. Levesque

757 citations of their works from 757 papers of our library (of the 149,439 whose bibliographies we read) · 206 with a page here, the most cited first · page 1 of 3 · 1–100 · [← Hector J. Levesque](https://md.research.elamur.ai/free/en/authors/hector-j-levesque/)

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1. **[BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://md.research.elamur.ai/free/en/papers/devlin-2019-bert-pre-training-deep-bidirectional/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Jacob Devlin, Ming-Wei Chang, Kenton Lee, et al.*2019 · 56,209 citations*
2. [**Language Models are Few-Shot Learners arXiv:2005.14165**Tom B. Brown, Benjamin Mann, Nick Ryder, et al.*2020 · 14,178 citations*](https://md.research.elamur.ai/free/en/papers/brown-2020-language-models-few-shot-learners/)
3. **[BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://md.research.elamur.ai/free/en/papers/lewis-2020-bart-denoising-sequence-sequence-pre/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Mike Lewis, Yinhan Liu, Naman Goyal, et al.*2020 · 12,856 citations*
4. [**Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer arXiv:1910.10683**Colin Raffel, Noam Shazeer, Adam Roberts, et al.*2019 · 11,325 citations*](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)
5. [**RoBERTa: A Robustly Optimized BERT Pretraining Approach arXiv:1907.11692**Yinhan Liu, Myle Ott, Naman Goyal, et al.*2019 · 9,837 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-roberta-robustly-optimized-bert-pretraining/)
6. **[GLUE: A Multi-Task Benchmark and Analysis Platform for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/wang-2018-glue-multi-task-benchmark-analysis/) [EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP](https://md.research.elamur.ai/free/en/venues/emnlp-workshop-blackboxnlp-analyzing-and-interpreting-neural-networks-for-nlp/)**Alex Wang, Amanpreet Singh, Julian Michael, et al.*2018 · 7,276 citations*
7. **[Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing](https://md.research.elamur.ai/free/en/papers/liu-2021-pre-train-prompt-predict-systematic/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Pengfei Liu, Weizhe Yuan, Jinlan Fu, et al.*2021 · 5,423 citations*
8. [**PaLM: Scaling Language Modeling with Pathways arXiv:2204.02311**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
9. **[The Power of Scale for Parameter-Efficient Prompt Tuning](https://md.research.elamur.ai/free/en/papers/lester-2021-power-scale-parameter-efficient-prompt/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Brian Lester, Rami Al-Rfou, Noah Constant*2021 · 4,287 citations*
10. **[Attention, Intentions, and the Structure of Discourse](https://md.research.elamur.ai/free/en/papers/grosz-1986-attention-intentions-structure-discourse/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Barbara J. Grosz, Candace L. Sidner*1986 · 2,929 citations*
11. [**ALBERT: A Lite BERT for Self-supervised Learning of Language Representations arXiv:1909.11942**Zhenzhong Lan, Mingda Chen, Sebastian Goodman, et al.*2019 · 2,806 citations*](https://md.research.elamur.ai/free/en/papers/lan-2019-albert-lite-bert-self-supervised/)
12. **[SpanBERT: Improving Pre-training by Representing and Predicting Spans](https://md.research.elamur.ai/free/en/papers/joshi-2019-spanbert-improving-pre-training-representing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Mandar Joshi, Danqi Chen, Yinhan Liu, et al.*2019 · 2,616 citations*
13. **[TinyBERT: Distilling BERT for Natural Language Understanding](https://md.research.elamur.ai/free/en/papers/jiao-2020-tinybert-distilling-bert-natural-language/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Xiaoqi Jiao, Yichun Yin, Lifeng Shang, et al.*2020 · 2,378 citations*
14. [**SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems arXiv:1905.00537**Alex Wang, Yada Pruksachatkun, Nikita Nangia, et al.*2019 · 2,227 citations*](https://md.research.elamur.ai/free/en/papers/wang-2019-superglue-stickier-benchmark-general-purpose/)
15. [**Finetuned Language Models Are Zero-Shot Learners arXiv:2109.01652**Jason Wei, Maarten Bosma, Vincent Y. Zhao, et al.*2021 · 1,954 citations*](https://md.research.elamur.ai/free/en/papers/wei-2021-finetuned-language-models-zero-shot/)
16. [**DeBERTa: Decoding-enhanced BERT with Disentangled Attention arXiv:2006.03654**Pengcheng He, Xiaodong Liu, Jianfeng Gao, et al.*2020 · 1,828 citations*](https://md.research.elamur.ai/free/en/papers/he-2020-deberta-decoding-enhanced-bert-disentangled/)
17. [**OPT: Open Pre-trained Transformer Language Models arXiv:2205.01068**Susan Zhang, Stephen Roller, Naman Goyal, et al.*2022 · 1,755 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-opt-open-pre-trained-transformer/)
18. **[CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledge](https://md.research.elamur.ai/free/en/papers/talmor-2019-commonsenseqa-question-answering-challenge-targeting/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Alon Talmor, Jonathan Herzig, Nicholas Lourie, et al.*2019 · 1,676 citations*
19. [**Multitask Prompted Training Enables Zero-Shot Task Generalization arXiv:2110.08207**Victor Sanh, Albert Webson, Colin Raffel, et al.*2021 · 1,612 citations*](https://md.research.elamur.ai/free/en/papers/sanh-2021-multitask-prompted-training-enables-zero/)
20. **[Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/min-2021-recent-advances-natural-language-processing/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Bonan Min, Hayley Ross, Elior Sulem, et al.*2021 · 1,556 citations*
21. **[Rethinking the Role of Demonstrations: What Makes In-Context Learning Work?](https://md.research.elamur.ai/free/en/papers/min-2022-rethinking-role-demonstrations-what-makes/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Sewon Min, Xinxi Lyu, Ari Holtzman, et al.*2022 · 1,444 citations*
22. [**Unified Language Model Pre-training for Natural Language Understanding and Generation arXiv:1905.03197**Li Dong, Nan Yang, Wenhui Wang, et al.*2019 · 1,422 citations*](https://md.research.elamur.ai/free/en/papers/dong-2019-unified-language-model-pre-training/)
23. [**LaMDA: Language Models for Dialog Applications arXiv:2201.08239**Romal Thoppilan, Daniel De Freitas, Jamie Hall, et al.*2022 · 1,344 citations*](https://md.research.elamur.ai/free/en/papers/thoppilan-2022-lamda-language-models-dialog-applications/)
24. [**DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing arXiv:2111.09543**Pengcheng He, Jianfeng Gao, Weizhu Chen*2021 · 1,320 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-debertav3-improving-deberta-using-electra/)
25. [**CTRL: A Conditional Transformer Language Model for Controllable Generation arXiv:1909.05858**Nitish Shirish Keskar, Bryan McCann, Lav R. Varshney, et al.*2019 · 1,291 citations*](https://md.research.elamur.ai/free/en/papers/keskar-2019-ctrl-conditional-transformer-language-model/)
26. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,287 citations*
27. **[It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learners](https://md.research.elamur.ai/free/en/papers/schick-2021-it-s-not-just-size/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Timo Schick, Hinrich Schütze*2021 · 1,132 citations*
28. **[Bias and Fairness in Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/gallegos-2023-bias-fairness-large-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Isabel O. Gallegos, Ryan A. Rossi, Joe Barrow, et al.*2023 · 1,082 citations*
29. **[GPT Understands, Too](https://md.research.elamur.ai/free/en/papers/liu-2021-gpt-understands-too/) [AI Open](https://md.research.elamur.ai/free/en/venues/ai-open/)**Xiao Liu, Yanan Zheng, Zhengxiao Du, et al.*2021 · 1,054 citations*
30. **[MobileBERT: a Compact Task-Agnostic BERT for Resource-Limited Devices](https://md.research.elamur.ai/free/en/papers/sun-2020-mobilebert-compact-task-agnostic-bert/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Zhiqing Sun, Hongkun Yu, Xiaodan Song, et al.*2020 · 1,016 citations*
31. [**PaLM 2 Technical Report arXiv:2305.10403**Rohan Anil, Andrew M. Dai, Orhan Firat, et al.*2023 · 1,000 citations*](https://md.research.elamur.ai/free/en/papers/anil-2023-palm-2-technical-report/)
32. [**AdapterFusion: Non-Destructive Task Composition for Transfer Learning arXiv:2005.00247 · Proceedings of EACL 2021**Jonas Pfeiffer, Aishwarya Kamath, Andreas Rücklé, et al.*2020 · 958 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterfusion-non-destructive-task-composition/)
33. [**MPNet: Masked and Permuted Pre-training for Language Understanding arXiv:2004.09297**Kaitao Song, Xu Tan, Tao Qin, et al.*2020 · 957 citations*](https://md.research.elamur.ai/free/en/papers/song-2020-mpnet-masked-permuted-pre-training/)
34. [**ChatGPT: Jack of all trades, master of none arXiv:2302.10724 · Information Fusion 101,861 (2023)**Jan Kocon, Igor Cichecki, Oliwier Kaszyca, et al.*2023 · 866 citations*](https://md.research.elamur.ai/free/en/papers/kocon-2023-chatgpt-jack-all-trades-master/)
35. **[Gender Bias in Coreference Resolution](https://md.research.elamur.ai/free/en/papers/rudinger-2018-gender-bias-coreference-resolution/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Rachel Rudinger, Jason Naradowsky, Brian Leonard, et al.*2018 · 864 citations*
36. **[UNIFIEDQA: Crossing Format Boundaries with a Single QA System](https://md.research.elamur.ai/free/en/papers/khashabi-2020-unifiedqa-crossing-format-boundaries-single/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Daniel Khashabi, Sewon Min, Tushar Khot, et al.*2020 · 857 citations*
37. [**GLM-130B: An Open Bilingual Pre-trained Model arXiv:2210.02414**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 786 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
38. **[Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 754 citations*
39. **[IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, et al.*2020 · 732 citations*
40. **[Explain Yourself! Leveraging Language Models for Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/rajani-2019-explain-yourself-leveraging-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Nazneen Fatema Rajani, Bryan McCann, Caiming Xiong, et al.*2019 · 701 citations*
41. [**BloombergGPT: A Large Language Model for Finance arXiv:2303.17564**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 618 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
42. [**What do you learn from context? Probing for sentence structure in contextualized word representations arXiv:1905.06316**Ian Tenney, Patrick Xia, Berlin Chen, et al.*2019 · 602 citations*](https://md.research.elamur.ai/free/en/papers/tenney-2019-learn-context-probing-sentence-structure/)
43. **[Evaluating Gender Bias in Machine Translation](https://md.research.elamur.ai/free/en/papers/stanovsky-2019-evaluating-gender-bias-machine-translation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gabriel Stanovsky, Noah A. Smith, Luke Zettlemoyer*2019 · 593 citations*
44. [**Towards a Human-like Open-Domain Chatbot arXiv:2001.09977**Daniel Adiwardana, Minh-Thang Luong, David R. So, et al.*2020 · 592 citations*](https://md.research.elamur.ai/free/en/papers/adiwardana-2020-towards-human-like-open-domain/)
45. **[A Robustly Optimized BERT Pre-training Approach with Post-training](https://md.research.elamur.ai/free/en/papers/zhuang-2021-robustly-optimized-bert-pre-training/) [Chinese National Conference on Computational Linguistics](https://md.research.elamur.ai/free/en/venues/ccl/)**Liu Zhuang, Lin Wayne, Shi Ya, et al.*2021 · 587 citations*
46. [**A Categorical Archive of ChatGPT Failures arXiv:2302.03494**Ali Borji*2023 · 559 citations*](https://md.research.elamur.ai/free/en/papers/borji-2023-categorical-archive-chatgpt-failures/)
47. [**The Natural Language Decathlon: Multitask Learning as Question Answering arXiv:1806.08730**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 545 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
48. [**Cosmos QA: Machine Reading Comprehension with Contextual Commonsense Reasoning**Lifu Huang, Ronan Le Bras, Chandra Bhagavatula, et al.*2019 · 544 citations*](https://md.research.elamur.ai/free/en/papers/huang-2019-cosmos-qa-machine-reading-comprehension/)
49. [**Evaluating Models' Local Decision Boundaries via Contrast Sets**Matt Gardner, Yoav Artzi, Victoria Basmov, et al.*2020 · 531 citations*](https://md.research.elamur.ai/free/en/papers/gardner-2020-evaluating-models-local-decision-boundaries/)
50. **[Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence](https://md.research.elamur.ai/free/en/papers/davis-2015-commonsense-reasoning-commonsense-knowledge-artificial/) [Communications of the ACM](https://md.research.elamur.ai/free/en/venues/communications-of-the-acm/)**Ernest Davis, Gary Marcus*2015 · 508 citations*
51. [**Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks arXiv:1811.01088**Jason Phang, Thibault Févry, Samuel R. Bowman*2018 · 503 citations*](https://md.research.elamur.ai/free/en/papers/phang-2018-sentence-encoders-stilts-supplementary-training/)
52. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts arXiv:2112.06905**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
53. **[XCOPA: A Multilingual Dataset for Causal Commonsense Reasoning](https://md.research.elamur.ai/free/en/papers/ponti-2020-xcopa-multilingual-dataset-causal-commonsense/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Edoardo Maria Ponti, Goran Glavaš, Olga Majewska, et al.*2020 · 469 citations*
54. [**CLEVR: A Diagnostic Dataset for Compositional Language and Elementary Visual Reasoning arXiv:1612.06890**Justin Johnson, Bharath Hariharan, Laurens van der Maaten, et al.*2016 · 464 citations*](https://md.research.elamur.ai/free/en/papers/johnson-2016-clevr-diagnostic-dataset-compositional-language/)
55. [**FreeLB: Enhanced Adversarial Training for Natural Language Understanding arXiv:1909.11764**Chen Zhu, Yu Cheng, Zhe Gan, et al.*2019 · 452 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2019-freelb-enhanced-adversarial-training-natural/)
56. **[Computational Generation of Referring Expressions: A Survey](https://md.research.elamur.ai/free/en/papers/krahmer-2012-computational-generation-referring-expressions-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Emiel Krahmer, Kees van Deemter*2012 · 433 citations*
57. **[Commonsense Knowledge Mining from Pretrained Models](https://md.research.elamur.ai/free/en/papers/davison-2019-commonsense-knowledge-mining-pretrained-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Joe Davison, Joshua Feldman, Alexander Rush*2019 · 413 citations*
58. [**True Few-Shot Learning with Language Models arXiv:2105.11447**Ethan Perez, Douwe Kiela, Kyunghyun Cho*2021 · 412 citations*](https://md.research.elamur.ai/free/en/papers/perez-2021-true-few-shot-learning-language/)
59. **[Do Prompt-Based Models Really Understand the Meaning of Their Prompts?](https://md.research.elamur.ai/free/en/papers/webson-2022-do-prompt-based-models-really/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Albert Webson, Ellie Pavlick*2022 · 407 citations*
60. [**ReCoRD: Bridging the Gap between Human and Machine Commonsense Reading Comprehension arXiv:1810.12885**Sheng Zhang, Xiaodong Liu, Jingjing Liu, et al.*2018 · 388 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2018-record-bridging-gap-between-human/)
61. [**Augmented Language Models: a Survey arXiv:2302.07842**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 369 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
62. [**Mind the GAP: A Balanced Corpus of Gendered Ambiguous Pronouns**Kellie Webster, Marta Recasens, Vera Axelrod, et al.*2018 · 358 citations*](https://md.research.elamur.ai/free/en/papers/webster-2018-mind-gap-balanced-corpus-gendered/)
63. [**RobBERT: a Dutch RoBERTa-based Language Model arXiv:2001.06286**Pieter Delobelle, Thomas Winters, Bettina Berendt*2020 · 349 citations*](https://md.research.elamur.ai/free/en/papers/delobelle-2020-robbert-dutch-roberta-based-language/)
64. [**ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation arXiv:2107.02137**Yu Sun, Shuohuan Wang, Shikun Feng, et al.*2021 · 332 citations*](https://md.research.elamur.ai/free/en/papers/sun-2021-ernie-3-0-large/)
65. **[Language Is Not All You Need: Aligning Perception with Language Models](https://md.research.elamur.ai/free/en/papers/huang-2023-language-all-need-aligning-perception/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shaohan Huang, Li Dong, Wenhui Wang, et al.*2023 · 330 citations*
66. **[TIES-Merging: Resolving Interference When Merging Models](https://md.research.elamur.ai/free/en/papers/yadav-2023-ties-merging-resolving-interference-merging/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Prateek Yadav, Derek Tam, Leshem Choshen, et al.*2023 · 324 citations*
67. **[How many data points is a prompt worth?](https://md.research.elamur.ai/free/en/papers/scao-2021-how-many-data-points-is/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Teven Le Scao, Alexander Rush*2021 · 320 citations*
68. [**WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation arXiv:2201.05955**Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.*2022 · 317 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/)
69. [**Abductive Commonsense Reasoning**Chandra Bhagavatula, Ronan Le Bras, Chaitanya Malaviya, et al.*2019 · 309 citations*](https://md.research.elamur.ai/free/en/papers/bhagavatula-2019-abductive-commonsense-reasoning/)
70. **[Muppet: Massive Multi-task Representations with Pre-Finetuning](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-muppet-massive-multi-task-representations/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Armen Aghajanyan, Anchit Gupta, Akshat Shrivastava, et al.*2021 · 307 citations*
71. **[SPoT: Better Frozen Model Adaptation through Soft Prompt Transfer](https://md.research.elamur.ai/free/en/papers/vu-2022-spot-better-frozen-model-adaptation/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tu Vu, Brian Lester, Noah Constant, et al.*2022 · 306 citations*
72. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts arXiv:2306.04528**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/)
73. **[ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://md.research.elamur.ai/free/en/papers/yao-2022-zeroquant-efficient-affordable-post-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.*2022 · 304 citations*
74. **[SMART: Robust and Efficient Fine-Tuning for Pre-trained Natural Language Models through Principled Regularized Optimization](https://md.research.elamur.ai/free/en/papers/jiang-2020-smart-robust-efficient-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Haoming Jiang, Pengcheng He, Weizhu Chen, et al.*2020 · 304 citations*
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