---
title: "Who cites Attention Is All You Need — page 3"
description: "16,413 papers of the Elamur Research library cite «Attention Is All You Need», found in their bibliographies · page 3 of 10 · 201–300; the most cited first."
canonical: "https://research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/cited-by/page/3/"
---
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# Who cites «Attention Is All You Need»

16,413 papers of our library cite it (of the 140,884 whose bibliographies we read) · 993 with a page here, the most cited first · page 3 of 10 · 201–300 · [← the paper](https://md.research.elamur.ai/free/en/papers/vaswani-2017-attention-all-need/)

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201. [**The BEA-2019 Shared Task on Grammatical Error Correction**Christopher Bryant, Mariano Felice, Øistein E. Andersen, et al.*2019 · 530 citations*](https://md.research.elamur.ai/free/en/papers/bryant-2019-bea-2019-shared-task-grammatical/)
202. [**Simple, Scalable Adaptation for Neural Machine Translation**Ankur Bapna, Orhan Firat*2019 · 524 citations*](https://md.research.elamur.ai/free/en/papers/bapna-2019-simple-scalable-adaptation-neural-machine/)
203. [**The Natural Language Decathlon: Multitask Learning as Question Answering**Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, et al.*2018 · 523 citations*](https://md.research.elamur.ai/free/en/papers/mccann-2018-natural-language-decathlon-multitask-learning/)
204. [**DeCLUTR: Deep Contrastive Learning for Unsupervised Textual Representations**John Giorgi, Osvald Nitski, Bo Wang, et al.*2021 · 513 citations*](https://md.research.elamur.ai/free/en/papers/giorgi-2021-declutr-deep-contrastive-learning-unsupervised/)
205. [**Baichuan 2: Open Large-scale Language Models**Aiyuan Yang, Bin Xiao, Bingning Wang, et al.*2023 · 512 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-baichuan-2-open-large-scale/)
206. [**MLS: A Large-Scale Multilingual Dataset for Speech Research**Vineel Pratap, Qiantong Xu, Anuroop Sriram, et al.*2020 · 510 citations*](https://md.research.elamur.ai/free/en/papers/pratap-2020-mls-large-scale-multilingual-dataset/)
207. [**TabFact: A Large-scale Dataset for Table-based Fact Verification**Wenhu Chen, Hongmin Wang, Jianshu Chen, et al.*2019 · 508 citations*](https://md.research.elamur.ai/free/en/papers/chen-2019-tabfact-large-scale-dataset-table/)
208. [**Marian: Fast Neural Machine Translation in C++**Marcin Junczys-Dowmunt, Roman Grundkiewicz, Tomasz Dwojak, et al.*2018 · 506 citations*](https://md.research.elamur.ai/free/en/papers/junczys-dowmunt-2018-marian-fast-neural-machine-translation/)
209. [**OpenAssistant Conversations — Democratizing Large Language Model Alignment**Andreas Köpf, Yannic Kilcher, Dimitri von Rütte, et al.*2023 · 505 citations*](https://md.research.elamur.ai/free/en/papers/kopf-2023-openassistant-conversations-democratizing-large-language/)
210. [**Multilingual Machine Translation with Large Language Models: Empirical Results and Analysis**Wenhao Zhu, Hongyi Liu, Qingxiu Dong, et al.*2024 · 503 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-multilingual-machine-translation-large-language/)
211. [**FLAT: Chinese NER Using Flat-Lattice Transformer**Xiaonan Li, Hang Yan, Xipeng Qiu, et al.*2020 · 503 citations*](https://md.research.elamur.ai/free/en/papers/li-2020-flat-chinese-ner-flat-lattice/)
212. [**Neural Text Generation with Unlikelihood Training**Sean Welleck, Ilia Kulikov, Stephen Roller, et al.*2019 · 498 citations*](https://md.research.elamur.ai/free/en/papers/welleck-2019-neural-text-generation-unlikelihood-training/)
213. [**Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks**Jason Phang, Thibault Févry, Samuel R. Bowman*2018 · 490 citations*](https://md.research.elamur.ai/free/en/papers/phang-2018-sentence-encoders-stilts-supplementary-training/)
214. [**Phi-4 Technical Report**Marah Abdin, Jyoti Aneja, Harkirat Behl, et al.*2024 · 489 citations*](https://md.research.elamur.ai/free/en/papers/abdin-2024-phi-4-technical-report/)
215. [**DeeBERT: Dynamic Early Exiting for Accelerating BERT Inference**Ji Xin, Raphael Tang, Jaejun Lee, et al.*2020 · 480 citations*](https://md.research.elamur.ai/free/en/papers/xin-2020-deebert-dynamic-early-exiting-accelerating/)
216. [**ToTTo: A Controlled Table-To-Text Generation Dataset**Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, et al.*2020 · 479 citations*](https://md.research.elamur.ai/free/en/papers/parikh-2020-totto-controlled-table-text-generation/)
217. [**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/)
218. [**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/)
219. [**Inference-Time Intervention: Eliciting Truthful Answers from a Language Model**Kenneth Li, Oam Patel, Fernanda Viegas, et al.*2023 · 473 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-inference-time-intervention-eliciting-truthful/)
220. [**Improving Massively Multilingual Neural Machine Translation and Zero-Shot Translation**Biao Zhang, Philip Williams, Ivan Titov, et al.*2020 · 472 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2020-improving-massively-multilingual-neural-machine/)
221. [**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/)
222. [**Beto, Bentz, Becas: The Surprising Cross-Lingual Effectiveness of BERT**Shijie Wu, Mark Dredze*2019 · 466 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-beto-bentz-becas-surprising-cross/)
223. [**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/)
224. [**CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X**Qinkai Zheng, Xiao Xia, Xu Zou, et al.*2023 · 462 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2023-codegeex-pre-trained-model-code/)
225. [**Hierarchical Transformers for Multi-Document Summarization**Yang Liu, Mirella Lapata*2019 · 462 citations*](https://md.research.elamur.ai/free/en/papers/liu-2019-hierarchical-transformers-multi-document-summarization/)
226. [**From Zero to Hero: On the Limitations of Zero-Shot Language Transfer with Multilingual Transformers**Anne Lauscher, Vinit Ravishankar, Ivan Vulić, et al.*2020 · 461 citations*](https://md.research.elamur.ai/free/en/papers/lauscher-2020-zero-hero-limitations-zero-shot/)
227. [**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/)
228. [**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/)
229. [**Are All Languages Created Equal in Multilingual BERT?**Shijie Wu, Mark Dredze*2020 · 458 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-are-all-languages-created-equal/)
230. [**Graph-to-Sequence Learning using Gated Graph Neural Networks**Daniel Beck, Gholamreza Haffari, Trevor Cohn*2018 · 456 citations*](https://md.research.elamur.ai/free/en/papers/beck-2018-graph-sequence-learning-gated-graph/)
231. [**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/)
232. [**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/)
233. [**Meta-Learning for Low-Resource Neural Machine Translation**Jiatao Gu, Yong Wang, Yun Chen, et al.*2018 · 451 citations*](https://md.research.elamur.ai/free/en/papers/gu-2018-meta-learning-low-resource-neural/)
234. [**CogView: Mastering Text-to-Image Generation via Transformers**Ming Ding, Zhuoyi Yang, Wenyi Hong, et al.*2021 · 446 citations*](https://md.research.elamur.ai/free/en/papers/ding-2021-cogview-mastering-text-image/)
235. [**HIBERT: Document Level Pre-training of Hierarchical Bidirectional Transformers for Document Summarization**Xingxing Zhang, Furu Wei, Ming Zhou*2019 · 446 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2019-hibert-document-level-pre-training/)
236. [**Context-Aware Neural Machine Translation Learns Anaphora Resolution**Elena Voita, Pavel Serdyukov, Rico Sennrich, et al.*2018 · 446 citations*](https://md.research.elamur.ai/free/en/papers/voita-2018-context-aware-neural-machine-translation/)
237. [**FinGPT: Open-Source Financial Large Language Models**Hongyang Yang, Xiao-Yang Liu, Christina Dan Wang*2023 · 445 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-fingpt-open-source-financial-large/)
238. [**GeDi: Generative Discriminator Guided Sequence Generation**Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann, et al.*2021 · 444 citations*](https://md.research.elamur.ai/free/en/papers/krause-2021-gedi-generative-discriminator-guided-sequence/)
239. [**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/)
240. [**Masked Language Model Scoring**Julian Salazar, Davis Liang, Toan Q. Nguyen, et al.*2020 · 444 citations*](https://md.research.elamur.ai/free/en/papers/salazar-2020-masked-language-model-scoring/)
241. [**Mathematical Capabilities of ChatGPT**Simon Frieder, Luca Pinchetti, Alexis Chevalier, et al.*2023 · 443 citations*](https://md.research.elamur.ai/free/en/papers/frieder-2023-mathematical-capabilities-chatgpt/)
242. [**Has Machine Translation Achieved Human Parity? A Case for Document-level Evaluation**Samuel Läubli, Rico Sennrich, Martin Volk*2018 · 443 citations*](https://md.research.elamur.ai/free/en/papers/laubli-2018-has-machine-translation-achieved-human/)
243. [**Pretrained Transformers Improve Out-of-Distribution Robustness**Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, et al.*2020 · 442 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2020-pretrained-transformers-improve-out-distribution/)
244. [**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/)
245. [**Measuring Coding Challenge Competence With APPS**Dan Hendrycks, Steven Basart, Saurav Kadavath, et al.*2021 · 438 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-measuring-coding-challenge-competence-apps/)
246. [**Fast Lexically Constrained Decoding with Dynamic Beam Allocation for Neural Machine Translation**Matt Post, David Vilar*2018 · 437 citations*](https://md.research.elamur.ai/free/en/papers/post-2018-fast-lexically-constrained-decoding-dynamic/)
247. [**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/)
248. [**MuST-C: a Multilingual Speech Translation Corpus**Mattia A. Di Gangi, Roldano Cattoni, Luisa Bentivogli, et al.*2019 · 436 citations*](https://md.research.elamur.ai/free/en/papers/gangi-2019-must-c-multilingual-speech-translation/)
249. [**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/)
250. [**Unsupervised Statistical Machine Translation**Mikel Artetxe, Gorka Labaka, Eneko Agirre*2018 · 432 citations*](https://md.research.elamur.ai/free/en/papers/artetxe-2018-unsupervised-statistical-machine-translation/)
251. [**Analyzing the Structure of Attention in a Transformer Language Model**Jesse Vig, Yonatan Belinkov*2019 · 430 citations*](https://md.research.elamur.ai/free/en/papers/vig-2019-analyzing-structure-attention-transformer-language/)
252. [**Experience Grounds Language**Yonatan Bisk, Ari Holtzman, Jesse Thomason, et al.*2020 · 429 citations*](https://md.research.elamur.ai/free/en/papers/bisk-2020-experience-grounds-language/)
253. [**Sequicity: Simplifying Task-oriented Dialogue Systems with Single Sequence-to-Sequence Architectures**Wenqiang Lei, Xisen Jin, Min-Yen Kan, et al.*2018 · 426 citations*](https://md.research.elamur.ai/free/en/papers/lei-2018-sequicity-simplifying-task-oriented-dialogue/)
254. [**Efficiently Teaching an Effective Dense Retriever with Balanced Topic Aware Sampling**Sebastian Hofstatter, Sheng-Chieh Lin, Jheng-Hong Yang, et al.*2021 · 425 citations*](https://md.research.elamur.ai/free/en/papers/hofstatter-2021-efficiently-teaching-effective-dense-retriever/)
255. [**Controllable Abstractive Summarization**Angela Fan, David Grangier, Michael Auli*2018 · 423 citations*](https://md.research.elamur.ai/free/en/papers/fan-2018-controllable-abstractive-summarization/)
256. [**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/)
257. [**Diffusion-LM Improves Controllable Text Generation**Xiang Lisa Li, John Thickstun, Ishaan Gulrajani, et al.*2022 · 421 citations*](https://md.research.elamur.ai/free/en/papers/li-2022-diffusion-lm-improves-controllable-text/)
258. [**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/)
259. [**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/)
260. [**Template-Based Named Entity Recognition Using BART**Leyang Cui, Yu Wu, Jian Liu, et al.*2021 · 420 citations*](https://md.research.elamur.ai/free/en/papers/cui-2021-template-based-named-entity-recognition/)
261. [**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/)
262. [**Understanding Factuality in Abstractive Summarization with FRANK: A Benchmark for Factuality Metrics**Artidoro Pagnoni, Vidhisha Balachandran, Yulia Tsvetkov*2021 · 417 citations*](https://md.research.elamur.ai/free/en/papers/pagnoni-2021-understanding-factuality-abstractive-summarization-frank/)
263. [**Pay Less Attention with Lightweight and Dynamic Convolutions**Felix Wu, Angela Fan, Alexei Baevski, et al.*2019 · 416 citations*](https://md.research.elamur.ai/free/en/papers/wu-2019-pay-less-attention-lightweight-dynamic/)
264. [**75 Languages, 1 Model: Parsing Universal Dependencies Universally**Dan Kondratyuk, Milan Straka*2019 · 415 citations*](https://md.research.elamur.ai/free/en/papers/kondratyuk-2019-75-languages-1-model-parsing/)
265. [**Kimi k1.5: Scaling Reinforcement Learning with LLMs**Kimi Team, Angang Du, Bofei Gao, et al.*2025 · 413 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k1-5-scaling-reinforcement/)
266. [**High Fidelity Neural Audio Compression**Alexandre Defossez, Jade Copet, Gabriel Synnaeve, et al.*2022 · 412 citations*](https://md.research.elamur.ai/free/en/papers/defossez-2022-high-fidelity-neural-audio-compression/)
267. [**Are Sixteen Heads Really Better than One?**Paul Michel, Omer Levy, Graham Neubig*2019 · 412 citations*](https://md.research.elamur.ai/free/en/papers/michel-2019-sixteen-heads-really-better-than/)
268. [**Document-Level Neural Machine Translation with Hierarchical Attention Networks**Lesly Miculicich, Dhananjay Ram, Nikolaos Pappas, et al.*2018 · 411 citations*](https://md.research.elamur.ai/free/en/papers/miculicich-2018-document-level-neural-machine-translation/)
269. [**A Computational Approach to Understanding Empathy Expressed in Text-Based Mental Health Support**Ashish Sharma, Adam Miner, David Atkins, et al.*2020 · 408 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2020-computational-approach-understanding-empathy-expressed/)
270. [**Multi-Turn Response Selection for Chatbots with Deep Attention Matching Network**Xiangyang Zhou, Lu Li, Daxiang Dong, et al.*2018 · 408 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2018-multi-turn-response-selection-chatbots/)
271. [**Automatic Detection of Generated Text is Easiest when Humans are Fooled**Daphne Ippolito, Daniel Duckworth, Chris Callison-Burch, et al.*2019 · 406 citations*](https://md.research.elamur.ai/free/en/papers/ippolito-2019-automatic-detection-generated-text-easiest/)
272. [**Training data-efficient image transformers & distillation through attention**Hugo Touvron, Matthieu Cord, Matthijs Douze, et al.*2020 · 405 citations*](https://md.research.elamur.ai/free/en/papers/touvron-2020-training-data-efficient-image-transformers/)
273. [**The FineWeb Datasets: Decanting the Web for the Finest Text Data at Scale**Guilherme Penedo, Hynek Kydlíček, Loubna Ben allal, et al.*2024 · 404 citations*](https://md.research.elamur.ai/free/en/papers/penedo-2024-fineweb-datasets-decanting-web-finest/)
274. [**Generating Sentences by Editing Prototypes**Kelvin Guu, Tatsunori B. Hashimoto, Yonatan Oren, et al.*2017 · 404 citations*](https://md.research.elamur.ai/free/en/papers/guu-2017-generating-sentences-editing-prototypes/)
275. [**Yi: Open Foundation Models by 01.AI**Alex Young, Bei Chen, Chao Li, et al.*2024 · 402 citations*](https://md.research.elamur.ai/free/en/papers/ai-2024-yi-open-foundation-models-01/)
276. [**Mem2Seq: Effectively Incorporating Knowledge Bases into End-to-End Task-Oriented Dialog Systems**Andrea Madotto, Chien-Sheng Wu, Pascale Fung*2018 · 401 citations*](https://md.research.elamur.ai/free/en/papers/madotto-2018-mem2seq-effectively-incorporating-knowledge-bases/)
277. [**Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning**Mitchell Gordon, Kevin Duh, Nicholas Andrews*2020 · 400 citations*](https://md.research.elamur.ai/free/en/papers/gordon-2020-compressing-bert-studying-effects-weight/)
278. [**How to Fine-Tune BERT for Text Classification?**Chi Sun, Xipeng Qiu, Yige Xu, et al.*2019 · 400 citations*](https://md.research.elamur.ai/free/en/papers/sun-2019-fine-tune-bert-text-classification/)
279. [**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/)
280. [**LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Models**Zhiqiang Hu, Lei Wang, Yihuai Lan, et al.*2023 · 398 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-llm-adapters-adapter-family-parameter/)
281. [**FLEURS: Few-shot Learning Evaluation of Universal Representations of Speech**Alexis Conneau, Min Ma, Simran Khanuja, et al.*2022 · 398 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2022-fleurs-few-shot-learning-evaluation/)
282. [**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/)
283. [**QMSum: A New Benchmark for Query-based Multi-domain Meeting Summarization**Ming Zhong, Da Yin, Tao Yu, et al.*2021 · 397 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2021-qmsum-new-benchmark-query-based/)
284. [**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/)
285. [**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/)
286. [**A Challenge Dataset and Effective Models for Aspect-Based Sentiment Analysis**Qingnan Jiang, Lei Chen, Ruifeng Xu, et al.*2019 · 393 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2019-challenge-dataset-effective-models-aspect/)
287. [**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/)
288. [**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/)
289. [**RULER: What's the Real Context Size of Your Long-Context Language Models?**Cheng-Ping Hsieh, Simeng Sun, Samuel Kriman, et al.*2024 · 386 citations*](https://md.research.elamur.ai/free/en/papers/hsieh-2024-ruler-s-real-context-size/)
290. [**German's Next Language Model**Branden Chan, Stefan Schweter, Timo Möller*2020 · 385 citations*](https://md.research.elamur.ai/free/en/papers/chan-2020-german-s-next-language-model/)
291. [**A Stack-Propagation Framework with Token-Level Intent Detection for Spoken Language Understanding**Libo Qin, Wanxiang Che, Yangming Li, et al.*2019 · 384 citations*](https://md.research.elamur.ai/free/en/papers/qin-2019-stack-propagation-framework-token-level/)
292. [**Deep Semantic Role Labeling with Self-Attention**Zhixing Tan, Mingxuan Wang, Jun Xie, et al.*2017 · 384 citations*](https://md.research.elamur.ai/free/en/papers/tan-2017-deep-semantic-role-labeling-self/)
293. [**Baseline Needs More Love: On Simple Word-Embedding-Based Models and Associated Pooling Mechanisms**Dinghan Shen, Guoyin Wang, Wenlin Wang, et al.*2018 · 383 citations*](https://md.research.elamur.ai/free/en/papers/shen-2018-baseline-needs-more-love-simple/)
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