---
title: "Who cites Illia Polosukhin — page 3"
description: "18,005 papers of the Elamur Research library cite the works of Illia Polosukhin, found in their bibliographies · page 3 of 12 · 201–300; the most cited…"
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---
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# Who cites Illia Polosukhin

18,396 citations of their works from 18,005 papers of our library (of the 140,884 whose bibliographies we read) · 1,116 with a page here, the most cited first · page 3 of 12 · 201–300 · [← Illia Polosukhin](https://md.research.elamur.ai/free/en/authors/illia-polosukhin/)

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201. [**OCR-free Document Understanding Transformer**Geewook Kim, Teakgyu Hong, Moonbin Yim, et al.*2021 · 580 citations*](https://md.research.elamur.ai/free/en/papers/kim-2021-ocr-free-document-understanding-transformer/)
202. [**Towards a Human-like Open-Domain Chatbot**Daniel Adiwardana, Minh-Thang Luong, David R. So, et al.*2020 · 573 citations*](https://md.research.elamur.ai/free/en/papers/adiwardana-2020-towards-human-like-open-domain/)
203. [**Multilingual Universal Sentence Encoder for Semantic Retrieval**Yinfei Yang, Daniel Cer, Amin Ahmad, et al.*2020 · 571 citations*](https://md.research.elamur.ai/free/en/papers/yang-2020-multilingual-universal-sentence-encoder-semantic/)
204. [**PhoBERT: Pre-trained language models for Vietnamese**Dat Quoc Nguyen, Anh Tuan Nguyen*2020 · 571 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2020-phobert-pre-trained-language-models/)
205. [**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/)
206. [**Leveraging Pre-trained Checkpoints for Sequence Generation Tasks**Sascha Rothe, Shashi Narayan, Aliaksei Severyn*2019 · 562 citations*](https://md.research.elamur.ai/free/en/papers/rothe-2019-leveraging-pre-trained-checkpoints-sequence/)
207. [**Plug and Play Language Models: A Simple Approach to Controlled Text Generation**Sumanth Dathathri, Andrea Madotto, Janice Lan, et al.*2019 · 562 citations*](https://md.research.elamur.ai/free/en/papers/dathathri-2019-plug-play-language-models-simple/)
208. [**Subword Regularization: Improving Neural Network Translation Models with Multiple Subword Candidates**Taku Kudo*2018 · 561 citations*](https://md.research.elamur.ai/free/en/papers/kudo-2018-subword-regularization-improving-neural-network/)
209. [**Galactica: A Large Language Model for Science**Ross Taylor, Marcin Kardas, Guillem Cucurull, et al.*2022 · 558 citations*](https://md.research.elamur.ai/free/en/papers/taylor-2022-galactica-large-language-model-science/)
210. [**Neural Legal Judgment Prediction in English**Ilias Chalkidis, Ion Androutsopoulos, Nikolaos Aletras*2019 · 558 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2019-neural-legal-judgment-prediction-english/)
211. [**gpt-oss-120b & gpt-oss-20b Model Card**OpenAI: Sandhini Agarwal, Lama Ahmad, Jason Ai, et al.*2025 · 555 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2025-gpt-oss-120b-gpt-oss/)
212. [**Massively Multilingual Neural Machine Translation in the Wild: Findings and Challenges**Naveen Arivazhagan, Ankur Bapna, Orhan Firat, et al.*2019 · 555 citations*](https://md.research.elamur.ai/free/en/papers/arivazhagan-2019-massively-multilingual-neural-machine-translation/)
213. [**Neural Text Summarization: A Critical Evaluation**Wojciech Kryscinski, Nitish Shirish Keskar, Bryan McCann, et al.*2019 · 555 citations*](https://md.research.elamur.ai/free/en/papers/kryscinski-2019-neural-text-summarization-critical-evaluation/)
214. [**The Best of Both Worlds: Combining Recent Advances in Neural Machine Translation**Mia Xu Chen, Orhan Firat, Ankur Bapna, et al.*2018 · 552 citations*](https://md.research.elamur.ai/free/en/papers/chen-2018-best-both-worlds-combining-recent/)
215. [**XTREME: A Massively Multilingual Multi-task Benchmark for Evaluating Cross-lingual Generalization**Junjie Hu, Sebastian Ruder, Aditya Siddhant, et al.*2020 · 551 citations*](https://md.research.elamur.ai/free/en/papers/hu-2020-xtreme-massively-multilingual-multi-task/)
216. [**How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection**Biyang Guo, Xin Zhang, Ziyuan Wang, et al.*2023 · 549 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-close-chatgpt-human-experts-comparison/)
217. [**Deterministic Non-Autoregressive Neural Sequence Modeling by Iterative Refinement**Jason Lee, Elman Mansimov, Kyunghyun Cho*2018 · 547 citations*](https://md.research.elamur.ai/free/en/papers/lee-2018-deterministic-non-autoregressive-neural-sequence/)
218. [**Reducing Transformer Depth on Demand with Structured Dropout**Angela Fan, Edouard Grave, Armand Joulin*2019 · 546 citations*](https://md.research.elamur.ai/free/en/papers/fan-2019-reducing-transformer-depth-demand-structured/)
219. [**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/)
220. [**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/)
221. [**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/)
222. [**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/)
223. [**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/)
224. [**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/)
225. [**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/)
226. [**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/)
227. [**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/)
228. [**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/)
229. [**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/)
230. [**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/)
231. [**Sparse, Dense, and Attentional Representations for Text Retrieval**Yi Luan, Jacob Eisenstein, Kristina Toutanova, et al.*2021 · 500 citations*](https://md.research.elamur.ai/free/en/papers/luan-2021-sparse-dense-attentional-representations-text/)
232. [**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/)
233. [**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/)
234. [**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/)
235. [**REPLUG: Retrieval-Augmented Black-Box Language Models**Weijia Shi, Sewon Min, Michihiro Yasunaga, et al.*2023 · 482 citations*](https://md.research.elamur.ai/free/en/papers/shi-2023-replug-retrieval-augmented-black-box/)
236. [**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/)
237. [**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/)
238. [**ColBERTv2: Effective and Efficient Retrieval via Lightweight Late Interaction**Keshav Santhanam, Omar Khattab, Jon Saad-Falcon, et al.*2022 · 477 citations*](https://md.research.elamur.ai/free/en/papers/santhanam-2022-colbertv2-effective-efficient-retrieval-lightweight/)
239. [**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/)
240. [**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/)
241. [**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/)
242. [**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/)
243. [**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/)
244. [**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/)
245. [**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/)
246. [**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/)
247. [**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/)
248. [**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/)
249. [**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/)
250. [**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/)
251. [**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/)
252. [**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/)
253. [**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/)
254. [**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/)
255. [**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/)
256. [**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/)
257. [**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/)
258. [**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/)
259. [**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/)
260. [**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/)
261. [**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/)
262. [**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/)
263. [**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/)
264. [**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/)
265. [**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/)
266. [**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/)
267. [**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/)
268. [**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/)
269. [**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/)
270. [**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/)
271. [**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/)
272. [**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/)
273. [**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/)
274. [**Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly**Nora Kassner, Hinrich Schütze*2020 · 431 citations*](https://md.research.elamur.ai/free/en/papers/kassner-2020-negated-misprimed-probes-pretrained-language/)
275. [**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/)
276. [**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/)
277. [**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/)
278. [**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/)
279. [**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/)
280. [**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/)
281. [**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/)
282. [**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/)
283. [**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/)
284. [**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/)
285. [**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/)
286. [**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/)
287. [**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/)
288. [**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/)
289. [**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/)
290. [**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/)
291. [**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/)
292. [**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/)
293. [**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/)
294. [**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/)
295. [**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/)
296. [**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/)
297. [**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/)
298. [**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/)
299. [**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/)
300. [**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/)

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