---
title: "Who cites Exploring the Limits of Transfer Learning with a…"
description: "7,433 papers of the Elamur Research library cite «Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer», found in their…"
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---
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# Who cites «Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer»

7,433 papers of our library cite it (of the 140,884 whose bibliographies we read) · 592 with a page here, the most cited first · page 2 of 6 · 101–200 · [← the paper](https://md.research.elamur.ai/free/en/papers/raffel-2019-exploring-limits-transfer-learning-unified/)

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101. [**Learning To Retrieve Prompts for In-Context Learning**Ohad Rubin, Jonathan Herzig, Jonathan Berant*2021 · 717 citations*](https://md.research.elamur.ai/free/en/papers/rubin-2021-learning-retrieve-prompts-context-learning/)
102. [**Crosslingual Generalization through Multitask Finetuning**Niklas Muennighoff, Thomas Wang, Lintang Sutawika, et al.*2022 · 692 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2022-crosslingual-generalization-through-multitask-finetuning/)
103. [**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/)
104. [**Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes**Cheng-Yu Hsieh, Chun-Liang Li, Chih-kuan Yeh, et al.*2023 · 684 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2023-distilling-step-step-outperforming-larger/)
105. [**DialoGPT: Large-Scale Generative Pre-training for Conversational Response Generation**Yizhe Zhang, Siqi Sun, Michel Galley, et al.*2019 · 684 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-dialogpt-large-scale-generative-pre/)
106. [**Large Language Models Are Human-Level Prompt Engineers**Yongchao Zhou, Andrei Ioan Muresanu, Ziwen Han, et al.*2022 · 677 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2022-large-language-models-human-level/)
107. [**KILT: a Benchmark for Knowledge Intensive Language Tasks**Fabio Petroni, Aleksandra Piktus, Angela Fan, et al.*2020 · 663 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-kilt-benchmark-knowledge-intensive-language/)
108. [**K-Adapter: Infusing Knowledge into Pre-Trained Models with Adapters**Ruize Wang, Duyu Tang, Nan Duan, et al.*2020 · 662 citations*](https://md.research.elamur.ai/free/en/papers/wang-2020-k-adapter-infusing-knowledge-pre/)
109. [**CCNet: Extracting High Quality Monolingual Datasets from Web Crawl Data**Guillaume Wenzek, Marie-Anne Lachaux, Alexis Conneau, et al.*2020 · 650 citations*](https://md.research.elamur.ai/free/en/papers/wenzek-2020-ccnet-extracting-high-quality-monolingual/)
110. [**CoCa: Contrastive Captioners are Image-Text Foundation Models**Jiahui Yu, Zirui Wang, Vijay Vasudevan, et al.*2022 · 646 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-coca-contrastive-captioners-image-text/)
111. [**LayoutLMv2: Multi-modal Pre-training for Visually-rich Document Understanding**Yang Xu, Yiheng Xu, Tengchao Lv, et al.*2021 · 623 citations*](https://md.research.elamur.ai/free/en/papers/xu-2021-layoutlmv2-multi-modal-pre-training/)
112. [**BloombergGPT: A Large Language Model for Finance**Shijie Wu, Ozan Irsoy, Steven Lu, et al.*2023 · 608 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-bloomberggpt-large-language-model-finance/)
113. [**Towards a Unified View of Parameter-Efficient Transfer Learning**Junxian He, Chunting Zhou, Xuezhe Ma, et al.*2021 · 604 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-towards-unified-view-parameter-efficient/)
114. [**Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Model**Shaden Smith, Mostofa Patwary, Brandon Norick, et al.*2022 · 595 citations*](https://md.research.elamur.ai/free/en/papers/smith-2022-using-deepspeed-megatron-train-megatron/)
115. [**Text Embeddings by Weakly-Supervised Contrastive Pre-training**Liang Wang, Nan Yang, Xiaolong Huang, et al.*2022 · 594 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-text-embeddings-weakly-supervised-contrastive/)
116. [**FNet: Mixing Tokens with Fourier Transforms**James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, et al.*2022 · 593 citations*](https://md.research.elamur.ai/free/en/papers/thorp-2022-fnet-mixing-tokens-fourier-transforms/)
117. [**The Flan Collection: Designing Data and Methods for Effective Instruction Tuning**Shayne Longpre, Le Hou, Tu Vu, et al.*2023 · 590 citations*](https://md.research.elamur.ai/free/en/papers/longpre-2023-flan-collection-designing-data-methods/)
118. [**Sentence-T5: Scalable Sentence Encoders from Pre-trained Text-to-Text Models**Jianmo Ni, Gustavo Hernandez Abrego, Noah Constant, et al.*2021 · 582 citations*](https://md.research.elamur.ai/free/en/papers/ni-2021-sentence-t5-scalable-sentence-encoders/)
119. [**AudioLM: a Language Modeling Approach to Audio Generation**Zalan Borsos, Raphael Marinier, Damien Vincent, et al.*2022 · 570 citations*](https://md.research.elamur.ai/free/en/papers/borsos-2022-audiolm-language-modeling-approach-audio/)
120. [**MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models**Longhui Yu, Weisen Jiang, Han Shi, et al.*2023 · 556 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/)
121. [**Document Ranking with a Pretrained Sequence-to-Sequence Model**Rodrigo Nogueira, Zhiying Jiang, Ronak Pradeep, et al.*2020 · 538 citations*](https://md.research.elamur.ai/free/en/papers/nogueira-2020-document-ranking-pretrained-sequence-sequence/)
122. [**Intrinsic Dimensionality Explains the Effectiveness of Language Model Fine-Tuning**Armen Aghajanyan, Sonal Gupta, Luke Zettlemoyer*2021 · 530 citations*](https://md.research.elamur.ai/free/en/papers/aghajanyan-2021-intrinsic-dimensionality-explains-effectiveness-language/)
123. [**Large Language Models Can Be Easily Distracted by Irrelevant Context**Freda Shi, Xinyun Chen, Kanishka Misra, et al.*2023 · 525 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-large-language-models-easily-distracted/)
124. [**Instruction Tuning for Large Language Models: A Survey**Shengyu Zhang, Linfeng Dong, Xiaoya Li, et al.*2023 · 515 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-instruction-tuning-large-language-models/)
125. [**LongBench: A Bilingual, Multitask Benchmark for Long Context Understanding**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2023 · 505 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-longbench-bilingual-multitask-benchmark/)
126. [**Sentiment Analysis in the Era of Large Language Models: A Reality Check**Wenxuan Zhang, Yue Deng, Bing Liu, et al.*2023 · 505 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-sentiment-analysis-era-large-language/)
127. [**Few-shot Learning with Multilingual Language Models**Xi Victoria Lin, Todor Mihaylov, Mikel Artetxe, et al.*2021 · 496 citations*](https://md.research.elamur.ai/free/en/papers/lin-2021-few-shot-learning-multilingual-language/)
128. [**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/)
129. [**ARBERT & MARBERT: Deep Bidirectional Transformers for Arabic**Muhammad Abdul-Mageed, AbdelRahim Elmadany, El Moatez Billah Nagoudi*2021 · 478 citations*](https://md.research.elamur.ai/free/en/papers/abdul-mageed-2021-arbert-marbert-deep-bidirectional-transformers/)
130. [**Enabling Large Language Models to Generate Text with Citations**Tianyu Gao, Howard Yen, Jiatong Yu, et al.*2023 · 477 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-enabling-large-language-models-generate/)
131. [**DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature**Eric Mitchell, Yoonho Lee, Alexander Khazatsky, et al.*2023 · 474 citations*](https://md.research.elamur.ai/free/en/papers/mitchell-2023-detectgpt-zero-shot-machine-generated/)
132. [**Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference time**Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.*2022 · 474 citations*](https://md.research.elamur.ai/free/en/papers/wortsman-2022-model-soups-averaging-weights-multiple/)
133. [**GLaM: Efficient Scaling of Language Models with Mixture-of-Experts**Nan Du, Yanping Huang, Andrew M. Dai, et al.*2021 · 474 citations*](https://md.research.elamur.ai/free/en/papers/du-2021-glam-efficient-scaling-language-models/)
134. [**Data Augmentation using Pre-trained Transformer Models**Varun Kumar, Ashutosh Choudhary, Eunah Cho*2020 · 468 citations*](https://md.research.elamur.ai/free/en/papers/kumar-2020-data-augmentation-using-pre-trained/)
135. [**PPT: Pre-trained Prompt Tuning for Few-shot Learning**Yuxian Gu, Xu Han, Zhiyuan Liu, et al.*2022 · 467 citations*](https://md.research.elamur.ai/free/en/papers/gu-2022-ppt-pre-trained-prompt-tuning/)
136. [**P-Tuning v2: Prompt Tuning Can Be Comparable to Fine-tuning Universally Across Scales and Tasks**Xiao Liu, Kaixuan Ji, Yicheng Fu, et al.*2021 · 464 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-p-tuning-v2-prompt-tuning/)
137. [**Masked Autoencoders Are Scalable Vision Learners**Kaiming He, Xinlei Chen, Saining Xie, et al.*2021 · 463 citations*](https://md.research.elamur.ai/free/en/papers/he-2021-masked-autoencoders-scalable-vision-learners/)
138. [**SimVLM: Simple Visual Language Model Pretraining with Weak Supervision**Zirui Wang, Jiahui Yu, Adams Wei Yu, et al.*2021 · 459 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-simvlm-simple-visual-language-model/)
139. [**LinkBERT: Pretraining Language Models with Document Links**Michihiro Yasunaga, Jure Leskovec, Percy Liang*2022 · 452 citations*](https://md.research.elamur.ai/free/en/papers/yasunaga-2022-linkbert-pretraining-language-models-document/)
140. [**OFA: Unifying Architectures, Tasks, and Modalities Through a Simple Sequence-to-Sequence Learning Framework**Peng Wang, An Yang, Rui Men, et al.*2022 · 452 citations*](https://md.research.elamur.ai/free/en/papers/wang-2022-ofa-unifying-architectures-tasks-modalities/)
141. [**ProphetNet: Predicting Future N-gram for Sequence-to-SequencePre-training**Weizhen Qi, Yu Yan, Yeyun Gong, et al.*2020 · 451 citations*](https://md.research.elamur.ai/free/en/papers/qi-2020-prophetnet-predicting-future-n-gram/)
142. [**Show your work: Scratchpads for intermediate computation with language models***2021 · 448 citations*](https://md.research.elamur.ai/free/en/papers/al-2021-show-your-work-scratchpads-intermediate/)
143. [**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/)
144. [**How Good is Your Tokenizer? On the Monolingual Performance of Multilingual Language Models**Phillip Rust, Jonas Pfeiffer, Ivan Vulić, et al.*2021 · 444 citations*](https://md.research.elamur.ai/free/en/papers/rust-2021-how-good-is-your-tokenizer/)
145. [**FreeLB: Enhanced Adversarial Training for Natural Language Understanding**Chen Zhu, Yu Cheng, Zhe Gan, et al.*2019 · 443 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2019-freelb-enhanced-adversarial-training-natural/)
146. [**MVBench: A Comprehensive Multi-modal Video Understanding Benchmark**Kunchang Li, Yali Wang, Yinan He, et al.*2023 · 441 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-mvbench-comprehensive-multi-modal-video/)
147. [**ShareGPT4V: Improving Large Multi-Modal Models with Better Captions**Lin Chen, Jinsong Li, Xiaoyi Dong, et al.*2023 · 441 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-sharegpt4v-improving-large-multi-modal/)
148. [**GPT-NER: Named Entity Recognition via Large Language Models**Shuhe Wang, Xiaofei Sun, Xiaoya Li, et al.*2025 · 436 citations*](https://md.research.elamur.ai/free/en/papers/wang-2025-gpt-ner-named-entity-recognition/)
149. [**The Falcon Series of Open Language Models**Ebtesam Almazrouei, Hamza Alobeidli, Abdulaziz Alshamsi, et al.*2023 · 436 citations*](https://md.research.elamur.ai/free/en/papers/almazrouei-2023-falcon-series-open-language-models/)
150. [**Document-Level Event Argument Extraction by Conditional Generation**Sha Li, Heng Ji, Jiawei Han*2021 · 432 citations*](https://md.research.elamur.ai/free/en/papers/li-2021-document-level-event-argument-extraction/)
151. [**Security and Privacy Challenges of Large Language Models: A Survey**Badhan Chandra Das, M. Hadi Amini, Yanzhao Wu*2024 · 422 citations*](https://md.research.elamur.ai/free/en/papers/das-2024-security-privacy-challenges-large-language/)
152. [**Ethical and social risks of harm from Language Models**Laura Weidinger, John Mellor, Maribeth Rauh, et al.*2021 · 421 citations*](https://md.research.elamur.ai/free/en/papers/weidinger-2021-ethical-social-risks-harm-language/)
153. [**Language Models as Zero-Shot Planners: Extracting Actionable Knowledge for Embodied Agents**Wenlong Huang, Pieter Abbeel, Deepak Pathak, et al.*2022 · 420 citations*](https://md.research.elamur.ai/free/en/papers/huang-2022-language-models-zero-shot-planners/)
154. [**Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs**Jinyang Li, Binyuan Hui, Ge Qu, et al.*2023 · 419 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-llm-already-serve-database-interface/)
155. [**ChatGPT Beyond English: Towards a Comprehensive Evaluation of Large Language Models in Multilingual Learning**Viet Dac Lai, Nghia Ngo, Amir Pouran Ben Veyseh, et al.*2023 · 418 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-chatgpt-beyond-english-comprehensive-evaluation/)
156. [**mPLUG-Owl: Modularization Empowers Large Language Models with Multimodality**Qinghao Ye, Haiyang Xu, Guohai Xu, et al.*2023 · 410 citations*](https://md.research.elamur.ai/free/en/papers/ye-2023-mplug-owl-modularization-empowers-large/)
157. [**BioMistral: A Collection of Open-Source Pretrained Large Language Models for Medical Domains**Yanis Labrak, Adrien Bazoge, Emmanuel Morin, et al.*2024 · 406 citations*](https://md.research.elamur.ai/free/en/papers/labrak-2024-biomistral-collection-open-source-pretrained/)
158. [**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/)
159. [**Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answering**Shamane Siriwardhana, Rivindu Weerasekera, Elliott Wen, et al.*2023 · 403 citations*](https://md.research.elamur.ai/free/en/papers/siriwardhana-2023-improving-domain-adaptation-retrieval-augmented/)
160. [**A Survey on Recent Approaches for Natural Language Processing in Low-Resource Scenarios**Michael A. Hedderich, Lukas Lange, Heike Adel, et al.*2021 · 399 citations*](https://md.research.elamur.ai/free/en/papers/hedderich-2021-survey-recent-approaches-natural-language/)
161. [**LongT5: Efficient Text-To-Text Transformer for Long Sequences**Mandy Guo, Joshua Ainslie, David Uthus, et al.*2022 · 398 citations*](https://md.research.elamur.ai/free/en/papers/guo-2022-longt5-efficient-text-text-transformer/)
162. [**Do Prompt-Based Models Really Understand the Meaning of Their Prompts?**Albert Webson, Ellie Pavlick*2022 · 396 citations*](https://md.research.elamur.ai/free/en/papers/webson-2022-do-prompt-based-models-really/)
163. [**A Unified Generative Framework for Aspect-based Sentiment Analysis**Hang Yan, Junqi Dai, Tuo Ji, et al.*2021 · 396 citations*](https://md.research.elamur.ai/free/en/papers/yan-2021-unified-generative-framework-aspect-based/)
164. [**Long Range Arena: A Benchmark for Efficient Transformers**Yi Tay, Mostafa Dehghani, Samira Abnar, et al.*2020 · 396 citations*](https://md.research.elamur.ai/free/en/papers/tay-2020-long-range-arena-benchmark-efficient/)
165. [**A Unified Generative Framework for Various NER Subtasks**Hang Yan, Tao Gui, Junqi Dai, et al.*2021 · 392 citations*](https://md.research.elamur.ai/free/en/papers/yan-2021-unified-generative-framework-various-ner/)
166. [**Query Rewriting in Retrieval-Augmented Large Language Models**Xinbei Ma, Yeyun Gong, Pengcheng He, et al.*2023 · 383 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/)
167. [**Enhancing Chat Language Models by Scaling High-quality Instructional Conversations**Ning Ding, Yulin Chen, Bokai Xu, et al.*2023 · 382 citations*](https://md.research.elamur.ai/free/en/papers/ding-2023-enhancing-chat-language-models-scaling/)
168. [**Retrieval Augmentation Reduces Hallucination in Conversation**Kurt Shuster, Spencer Poff, Moya Chen, et al.*2021 · 382 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2021-retrieval-augmentation-reduces-hallucination-conversation/)
169. [**Discourse-Aware Neural Extractive Text Summarization**Jiacheng Xu, Zhe Gan, Yu Cheng, et al.*2019 · 382 citations*](https://md.research.elamur.ai/free/en/papers/xu-2019-discourse-aware-neural-extractive-text/)
170. [**A Survey on Model Compression for Large Language Models**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/)
171. [**Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity**Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al.*2024 · 381 citations*](https://md.research.elamur.ai/free/en/papers/jeong-2024-adaptive-rag-learning-adapt-retrieval/)
172. [**UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training**Hangbo Bao, Li Dong, Furu Wei, et al.*2020 · 380 citations*](https://md.research.elamur.ai/free/en/papers/bao-2020-unilmv2-pseudo-masked-language-models/)
173. [**ETC: Encoding Long and Structured Inputs in Transformers**Joshua Ainslie, Santiago Ontañón, Chris Alberti, et al.*2020 · 377 citations*](https://md.research.elamur.ai/free/en/papers/ainslie-2020-etc-encoding-long-structured-inputs/)
174. [**Tool Learning with Foundation Models**Yujia Qin, Shengding Hu, Yankai Lin, et al.*2023 · 376 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-tool-learning-foundation-models/)
175. [**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/)
176. [**FastBERT: a Self-distilling BERT with Adaptive Inference Time**Weijie Liu, Peng Zhou, Zhiruo Wang, et al.*2020 · 374 citations*](https://md.research.elamur.ai/free/en/papers/liu-2020-fastbert-self-distilling-bert-adaptive/)
177. [**CogVLM: Visual Expert for Pretrained Language Models**Weihan Wang, Qingsong Lv, Wenmeng Yu, et al.*2023 · 373 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-cogvlm-visual-expert-pretrained/)
178. [**A Dataset of Information-Seeking Questions and Answers Anchored in Research Papers**Pradeep Dasigi, Kyle Lo, Iz Beltagy, et al.*2021 · 372 citations*](https://md.research.elamur.ai/free/en/papers/dasigi-2021-dataset-information-seeking-questions-answers/)
179. [**Text2Event: Controllable Sequence-to-Structure Generation for End-to-end Event Extraction**Yaojie Lu, Hongyu Lin, Jin Xu, et al.*2021 · 370 citations*](https://md.research.elamur.ai/free/en/papers/lu-2021-text2event-controllable-sequence-structure-generation/)
180. [**VILA: On Pre-training for Visual Language Models**Ji Lin, Hongxu Yin, Wei Ping, et al.*2023 · 367 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-vila-pre-training-visual-language/)
181. [**A Survey on Large Language Models for Code Generation**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 366 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/)
182. [**Augmented Language Models: a Survey**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 362 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
183. [**Knowledgeable Prompt-tuning: Incorporating Knowledge into Prompt Verbalizer for Text Classification**Shengding Hu, Ning Ding, Huadong Wang, et al.*2022 · 360 citations*](https://md.research.elamur.ai/free/en/papers/hu-2022-knowledgeable-prompt-tuning-incorporating-knowledge/)
184. [**TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue**Chien-Sheng Wu, Steven C.H. Hoi, Richard Socher, et al.*2020 · 360 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-tod-bert-pre-trained-natural/)
185. [**Datasets: A Community Library for Natural Language Processing**Quentin Lhoest, Albert Villanova del Moral, Yacine Jernite, et al.*2021 · 358 citations*](https://md.research.elamur.ai/free/en/papers/lhoest-2021-datasets-community-library-natural-language/)
186. [**BRIO: Bringing Order to Abstractive Summarization**Yixin Liu, Pengfei Liu, Dragomir Radev, et al.*2022 · 357 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-brio-bringing-order-abstractive-summarization/)
187. [**Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor**Or Honovich, Thomas Scialom, Omer Levy, et al.*2023 · 356 citations*](https://md.research.elamur.ai/free/en/papers/honovich-2023-unnatural-instructions-tuning-language-models/)
188. [**Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference**Benjamin Warner, Antoine Chaffin, Benjamin Clavié, et al.*2024 · 355 citations*](https://md.research.elamur.ai/free/en/papers/warner-2024-smarter-better-faster-longer-modern/)
189. [**Parameter-Efficient Transfer Learning with Diff Pruning**Demi Guo, Alexander Rush, Yoon Kim*2021 · 355 citations*](https://md.research.elamur.ai/free/en/papers/guo-2021-parameter-efficient-transfer-learning-diff/)
190. [**QuestEval: Summarization Asks for Fact-based Evaluation**Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier, et al.*2021 · 353 citations*](https://md.research.elamur.ai/free/en/papers/scialom-2021-questeval-summarization-asks-fact-based/)
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