---
title: "Who cites Yanqi Zhou — page 4"
description: "8,127 papers of the Elamur Research library cite the works of Yanqi Zhou, found in their bibliographies · page 4 of 7 · 301–400; the most cited first."
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---
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# Who cites Yanqi Zhou

8,538 citations of their works from 8,127 papers of our library (of the 140,884 whose bibliographies we read) · 669 with a page here, the most cited first · page 4 of 7 · 301–400 · [← Yanqi Zhou](https://md.research.elamur.ai/free/en/authors/yanqi-zhou/)

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301. [**LexGLUE: A Benchmark Dataset for Legal Language Understanding in English**Ilias Chalkidis; Abhik Jana; Dirk Hartung; Michael Bommarito; Ion Androutsopoulos; Daniel Martin Katz;, Nikolaos Aletras*2021 · 256 citations*](https://md.research.elamur.ai/free/en/papers/chalkidis-2021-lexglue-benchmark-dataset-legal-language/)
302. [**Re-evaluating Evaluation in Text Summarization**Manik Bhandari, Pranav Narayan Gour, Atabak Ashfaq, et al.*2020 · 256 citations*](https://md.research.elamur.ai/free/en/papers/bhandari-2020-re-evaluating-evaluation-text-summarization/)
303. [**Multilingual is not enough: BERT for Finnish**Antti Virtanen, Jenna Kanerva, Rami Ilo, et al.*2019 · 256 citations*](https://md.research.elamur.ai/free/en/papers/virtanen-2019-multilingual-enough-bert-finnish/)
304. [**Fine-Tuning LLaMA for Multi-Stage Text Retrieval**Xueguang Ma, Liang Wang, Nan Yang, et al.*2023 · 254 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-fine-tuning-llama-multi-stage/)
305. [**DeepSpeed-MoE: Advancing Mixture-of-Experts Inference and Training to Power Next-Generation AI Scale**Samyam Rajbhandari, Conglong Li, Zhewei Yao, et al.*2022 · 254 citations*](https://md.research.elamur.ai/free/en/papers/rajbhandari-2022-deepspeed-moe-advancing-mixture-experts/)
306. [**TAPEX: Table Pre-training via Learning a Neural SQL Executor**Qian Liu, Bei Chen, Jiaqi Guo, et al.*2021 · 254 citations*](https://md.research.elamur.ai/free/en/papers/liu-2021-tapex-table-pre-training-learning/)
307. [**An Empirical Survey of Data Augmentation for Limited Data Learning in NLP**Jiaao Chen, Derek Tam, Colin Raffel, et al.*2023 · 253 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-empirical-survey-data-augmentation-limited/)
308. [**GPT-RE: In-context Learning for Relation Extraction using Large Language Models**Zhen Wan, Fei Cheng, Zhuoyuan Mao, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/wan-2023-gpt-re-context-learning-relation/)
309. [**Societal Biases in Language Generation: Progress and Challenges**Emily Sheng, Kai-Wei Chang, Prem Natarajan, et al.*2021 · 252 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2021-societal-biases-language-generation-progress/)
310. [**Measuring and Reducing Gendered Correlations in Pre-trained Models**Kellie Webster, Xuezhi Wang, Ian Tenney, et al.*2020 · 252 citations*](https://md.research.elamur.ai/free/en/papers/webster-2020-measuring-reducing-gendered-correlations-pre/)
311. [**Survey of Low-Resource Machine Translation**Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, et al.*2022 · 251 citations*](https://md.research.elamur.ai/free/en/papers/haddow-2022-survey-low-resource-machine-translation/)
312. [**Generating Datasets with Pretrained Language Models**Timo Schick, Hinrich Schutze*2021 · 250 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-generating-datasets-pretrained-language-models/)
313. [**LeanDojo: Theorem Proving with Retrieval-Augmented Language Models**Kaiyu Yang, Aidan M. Swope, Alex Gu, et al.*2023 · 248 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-leandojo-theorem-proving-retrieval-augmented/)
314. [**Parameter-efficient Multi-task Fine-tuning for Transformers via Shared Hypernetworks**Rabeeh Karimi Mahabadi, Sebastian Ruder, Mostafa Dehghani, et al.*2021 · 248 citations*](https://md.research.elamur.ai/free/en/papers/mahabadi-2021-parameter-efficient-multi-task-fine/)
315. [**RedPajama: an Open Dataset for Training Large Language Models**Maurice Weber, Daniel Fu, Quentin Anthony, et al.*2024 · 247 citations*](https://md.research.elamur.ai/free/en/papers/weber-2024-redpajama-open-dataset-training-large/)
316. [**DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models**Boxin Wang, Weixin Chen, Hengzhi Pei, et al.*2023 · 246 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-decodingtrust-comprehensive-assessment-trustworthiness-gpt/)
317. [**Aya Model: An Instruction Finetuned Open-Access Multilingual Language Model**Ahmet Üstün, Viraat Aryabumi, Zheng-Xin Yong, et al.*2024 · 245 citations*](https://md.research.elamur.ai/free/en/papers/ustun-2024-aya-model-instruction-finetuned-open/)
318. [**NLP Evaluation in trouble: On the Need to Measure LLM Data Contamination for each Benchmark**Oscar Sainz, Jon Campos, Iker García-Ferrero, et al.*2023 · 245 citations*](https://md.research.elamur.ai/free/en/papers/sainz-2023-nlp-evaluation-trouble-need-measure/)
319. [**Ignore Previous Prompt: Attack Techniques For Language Models**Fabio Perez, Ian Ribeiro*2022 · 244 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-ignore-previous-prompt-attack-techniques/)
320. [**Structured Denoising Diffusion Models in Discrete State-Spaces**Jacob Austin, Daniel D. Johnson, Jonathan Ho, et al.*2021 · 244 citations*](https://md.research.elamur.ai/free/en/papers/austin-2021-structured-denoising-diffusion-models-discrete/)
321. [**Knowledge-Grounded Dialogue Generation with Pre-trained Language Models**Xueliang Zhao, Wei Wu, Can Xu, et al.*2020 · 244 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2020-knowledge-grounded-dialogue-generation-pre/)
322. [**Automatic Detection of Machine Generated Text: A Critical Survey**Ganesh Jawahar, Muhammad Abdul-Mageed, Laks Lakshmanan V.S.*2020 · 243 citations*](https://md.research.elamur.ai/free/en/papers/jawahar-2020-automatic-detection-machine-generated-text/)
323. [**CLUE: A Chinese Language Understanding Evaluation Benchmark**Liang Xu, Hai Hu, Xuanwei Zhang, et al.*2020 · 242 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-clue-chinese-language-understanding/)
324. [**Large Language Monkeys: Scaling Inference Compute with Repeated Sampling**Bradley Brown, Jordan Juravsky, Ryan Ehrlich, et al.*2024 · 241 citations*](https://md.research.elamur.ai/free/en/papers/brown-2024-large-language-monkeys-scaling-inference/)
325. [**Factuality Challenges in the Era of Large Language Models**Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, et al.*2023 · 241 citations*](https://md.research.elamur.ai/free/en/papers/augenstein-2023-factuality-challenges-era-large-language/)
326. [**Language Models can Solve Computer Tasks**Geunwoo Kim, Pierre Baldi, Stephen McAleer*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-language-models-solve-computer-tasks/)
327. [**ASQA: Factoid Questions Meet Long-Form Answers**Ivan Stelmakh, Yi Luan, Bhuwan Dhingra, et al.*2022 · 239 citations*](https://md.research.elamur.ai/free/en/papers/stelmakh-2022-asqa-factoid-questions-meet-long/)
328. [**Curriculum Learning for Natural Language Understanding**Benfeng Xu, Licheng Zhang, Zhendong Mao, et al.*2020 · 239 citations*](https://md.research.elamur.ai/free/en/papers/xu-2020-curriculum-learning-natural-language-understanding/)
329. [**MinTL: Minimalist Transfer Learning for Task-Oriented Dialogue Systems**Zhaojiang Lin, Andrea Madotto, Genta Indra Winata, et al.*2020 · 239 citations*](https://md.research.elamur.ai/free/en/papers/lin-2020-mintl-minimalist-transfer-learning-task/)
330. [**Knowledge Graph Based Synthetic Corpus Generation for Knowledge-Enhanced Language Model Pre-training**Oshin Agarwal, Heming Ge, Siamak Shakeri, et al.*2021 · 238 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2021-knowledge-graph-based-synthetic-corpus/)
331. [**Multimodal Few-Shot Learning with Frozen Language Models**Maria Tsimpoukelli, Jacob Menick, Serkan Cabi, et al.*2021 · 238 citations*](https://md.research.elamur.ai/free/en/papers/tsimpoukelli-2021-multimodal-few-shot-learning-frozen/)
332. [**OPT-IML: Scaling Language Model Instruction Meta Learning through the Lens of Generalization**Srinivasan Iyer, Xi Victoria Lin, Ramakanth Pasunuru, et al.*2022 · 237 citations*](https://md.research.elamur.ai/free/en/papers/iyer-2022-opt-iml-scaling-language-model/)
333. [**mGTE: Generalized Long-Context Text Representation and Reranking Models for Multilingual Text Retrieval**Xin Zhang, Yanzhao Zhang, Dingkun Long, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-mgte-generalized-long-context-text/)
334. [**COMET-ATOMIC 2020: On Symbolic and Neural Commonsense Knowledge Graphs**Jena D. Hwang, Chandra Bhagavatula, Ronan Le Bras, et al.*2020 · 235 citations*](https://md.research.elamur.ai/free/en/papers/hwang-2020-comet-atomic-2020-symbolic-neural/)
335. [**Generative Language Modeling for Automated Theorem Proving**Stanislas Polu, Ilya Sutskever*2020 · 235 citations*](https://md.research.elamur.ai/free/en/papers/polu-2020-generative-language-modeling-automated-theorem/)
336. [**BlenderBot 3: a deployed conversational agent that continually learns to responsibly engage**Kurt Shuster, Jing Xu, Mojtaba Komeili, et al.*2022 · 234 citations*](https://md.research.elamur.ai/free/en/papers/shuster-2022-blenderbot-3-deployed-conversational-agent/)
337. [**FUDGE: Controlled Text Generation With Future Discriminators**Kevin Yang, Dan Klein*2021 · 234 citations*](https://md.research.elamur.ai/free/en/papers/yang-2021-fudge-controlled-text-generation-future/)
338. [**Evaluating Open-Domain Question Answering in the Era of Large Language Models**Ehsan Kamalloo, Nouha Dziri, Charles Clarke, et al.*2023 · 233 citations*](https://md.research.elamur.ai/free/en/papers/kamalloo-2023-evaluating-open-domain-question-answering/)
339. [**Principle-Driven Self-Alignment of Language Models from Scratch with Minimal Human Supervision**Zhiqing Sun, Yikang Shen, Qinhong Zhou, et al.*2023 · 232 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-principle-driven-self-alignment-language/)
340. [**How Context Affects Language Models' Factual Predictions**Fabio Petroni, Patrick Lewis, Aleksandra Piktus, et al.*2020 · 231 citations*](https://md.research.elamur.ai/free/en/papers/petroni-2020-context-affects-language-models-factual/)
341. [**Hurdles to Progress in Long-form Question Answering**Kalpesh Krishna, Aurko Roy, Mohit Iyyer*2021 · 227 citations*](https://md.research.elamur.ai/free/en/papers/krishna-2021-hurdles-progress-long-form-question/)
342. [**Tower: An Open Multilingual Large Language Model for Translation-Related Tasks**Duarte M. Alves, Jose Pombal, Nuno M. Guerreiro, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/alves-2024-tower-open-multilingual-large-language/)
343. [**TrustLLM: Trustworthiness in Large Language Models**Yue Huang, Lichao Sun, Haoran Wang, et al.*2024 · 226 citations*](https://md.research.elamur.ai/free/en/papers/huang-2024-trustllm-trustworthiness-large-language-models/)
344. [**Prompt for Extraction? PAIE: Prompting Argument Interaction for Event Argument Extraction**Yubo Ma, Zehao Wang, Yixin Cao, et al.*2022 · 225 citations*](https://md.research.elamur.ai/free/en/papers/ma-2022-prompt-extraction-paie-prompting-argument/)
345. [**Not All Languages Are Created Equal in LLMs: Improving Multilingual Capability by Cross-Lingual-Thought Prompting**Haoyang Huang, Tianyi Tang, Dongdong Zhang, et al.*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-all-languages-created-equal-llms/)
346. [**Specializing Smaller Language Models towards Multi-Step Reasoning**Yao Fu, Hao Peng, Litu Ou, et al.*2023 · 222 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-specializing-smaller-language-models-towards/)
347. [**Beyond neural scaling laws: beating power law scaling via data pruning**Ben Sorscher, Robert Geirhos, Shashank Shekhar, et al.*2022 · 220 citations*](https://md.research.elamur.ai/free/en/papers/sorscher-2022-neural-scaling-laws-beating-power/)
348. [**Transformer Memory as a Differentiable Search Index**Yi Tay, Vinh Q. Tran, Mostafa Dehghani, et al.*2022 · 220 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-transformer-memory-differentiable-search-index/)
349. [**NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models**Chankyu Lee, Rajarshi Roy, Mengyao Xu, et al.*2024 · 219 citations*](https://md.research.elamur.ai/free/en/papers/lee-2024-nv-embed-improved-techniques-training/)
350. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 219 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
351. [**A Monolingual Approach to Contextualized Word Embeddings for Mid-Resource Languages**Pedro Javier Ortiz Suárez, Laurent Romary, Benoît Sagot*2020 · 219 citations*](https://md.research.elamur.ai/free/en/papers/suarez-2020-monolingual-approach-contextualized-word-embeddings/)
352. [**What Happens To BERT Embeddings During Fine-tuning?**Amil Merchant, Elahe Rahimtoroghi, Ellie Pavlick, et al.*2020 · 219 citations*](https://md.research.elamur.ai/free/en/papers/merchant-2020-what-happens-bert-embeddings-during/)
353. [**Jais and Jais-chat: Arabic-Centric Foundation and Instruction-Tuned Open Generative Large Language Models**Neha Sengupta, Sunil Kumar Sahu, Bokang Jia, et al.*2023 · 217 citations*](https://md.research.elamur.ai/free/en/papers/sengupta-2023-jais-jais-chat-arabic-centric/)
354. [**Structured Pruning Learns Compact and Accurate Models**Mengzhou Xia, Zexuan Zhong, Danqi Chen*2022 · 216 citations*](https://md.research.elamur.ai/free/en/papers/xia-2022-structured-pruning-learns-compact-accurate/)
355. [**Measuring Association Between Labels and Free-Text Rationales**Sarah Wiegreffe, Ana Marasović, Noah A. Smith*2021 · 215 citations*](https://md.research.elamur.ai/free/en/papers/wiegreffe-2021-measuring-association-between-labels-free/)
356. [**Syntactic Data Augmentation Increases Robustness to Inference Heuristics**Junghyun Min, R. Thomas McCoy, Dipanjan Das, et al.*2020 · 215 citations*](https://md.research.elamur.ai/free/en/papers/min-2020-syntactic-data-augmentation-increases-robustness/)
357. [**Maieutic Prompting: Logically Consistent Reasoning with Recursive Explanations**Jaehun Jung, Lianhui Qin, Sean Welleck, et al.*2022 · 213 citations*](https://md.research.elamur.ai/free/en/papers/jung-2022-maieutic-prompting-logically-consistent-reasoning/)
358. [**Evaluating the Logical Reasoning Ability of ChatGPT and GPT-4**Hanmeng Liu, Ruoxi Ning, Zhiyang Teng, et al.*2023 · 212 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-evaluating-logical-reasoning-ability-chatgpt/)
359. [**Fine-Grained Human Feedback Gives Better Rewards for Language Model Training**Zeqiu Wu, Yushi Hu, Weijia Shi, et al.*2023 · 212 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-fine-grained-human-feedback-gives/)
360. [**MADLAD-400: A Multilingual And Document-Level Large Audited Dataset**Sneha Kudugunta, Isaac Caswell, Biao Zhang, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/kudugunta-2023-madlad-400-multilingual-document-level/)
361. [**Teaching language models to support answers with verified quotes**Jacob Menick, Maja Trebacz, Vladimir Mikulik, et al.*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/menick-2022-teaching-language-models-support-answers/)
362. [**Large Language Model Is Not a Good Few-shot Information Extractor, but a Good Reranker for Hard Samples!**Yubo Ma, Yixin Cao, Yong Hong, et al.*2023 · 210 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-large-language-model-is-not/)
363. [**LLM2Vec: Large Language Models Are Secretly Powerful Text Encoders**Parishad BehnamGhader, Vaibhav Adlakha, Marius Mosbach, et al.*2024 · 209 citations*](https://md.research.elamur.ai/free/en/papers/behnamghader-2024-llm2vec-large-language-models-secretly/)
364. [**Dynamic Prompt Learning via Policy Gradient for Semi-structured Mathematical Reasoning**Pan Lu, Liang Qiu, Kai-Wei Chang, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/lu-2022-dynamic-prompt-learning-policy-gradient/)
365. [**PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change**Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.*2022 · 209 citations*](https://md.research.elamur.ai/free/en/papers/valmeekam-2022-planbench-extensible-benchmark-evaluating-large/)
366. [**Safe RLHF: Safe Reinforcement Learning from Human Feedback**Josef Dai, Xuehai Pan, Ruiyang Sun, et al.*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-safe-rlhf-safe-reinforcement-learning/)
367. [**Active Example Selection for In-Context Learning**Yiming Zhang, Shi Feng, Chenhao Tan*2022 · 207 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-active-example-selection-context-learning/)
368. [**Self-Diagnosis and Self-Debiasing: A Proposal for Reducing Corpus-Based Bias in NLP**Timo Schick, Sahana Udupa, Hinrich Schutze*2021 · 207 citations*](https://md.research.elamur.ai/free/en/papers/schick-2021-self-diagnosis-self-debiasing-proposal/)
369. [**Enabling Language Models to Fill in the Blanks**Chris Donahue, Mina Lee, Percy Liang*2020 · 207 citations*](https://md.research.elamur.ai/free/en/papers/donahue-2020-enabling-language-models-fill-blanks/)
370. [**Generating Training Data with Language Models: Towards Zero-Shot Language Understanding**Yu Meng, Jiaxin Huang, Yu Zhang, et al.*2022 · 206 citations*](https://md.research.elamur.ai/free/en/papers/meng-2022-generating-training-data-language-models/)
371. [**Challenges in Detoxifying Language Models**Johannes Welbl, Amelia Glaese, Jonathan Uesato, et al.*2021 · 206 citations*](https://md.research.elamur.ai/free/en/papers/welbl-2021-challenges-detoxifying-language-models/)
372. [**Explaining Answers with Entailment Trees**Bhavana Dalvi, Peter Jansen, Oyvind Tafjord, et al.*2021 · 206 citations*](https://md.research.elamur.ai/free/en/papers/dalvi-2021-explaining-answers-entailment-trees/)
373. [**Training Question Answering Models From Synthetic Data**Raul Puri, Ryan Spring, Mohammad Shoeybi, et al.*2020 · 206 citations*](https://md.research.elamur.ai/free/en/papers/puri-2020-training-question-answering-models-synthetic/)
374. [**Search-o1: Agentic Search-Enhanced Large Reasoning Models**Xiaoxi Li, Guanting Dong, Jiajie Jin, et al.*2025 · 205 citations*](https://md.research.elamur.ai/free/en/papers/li-2025-search-o1-agentic-search-enhanced/)
375. [**On the Origin of Hallucinations in Conversational Models: Is it the Datasets or the Models?**Nouha Dziri, Sivan Milton, Mo Yu, et al.*2022 · 205 citations*](https://md.research.elamur.ai/free/en/papers/dziri-2022-origin-hallucinations-conversational-models-is/)
376. [**Entities as Experts: Sparse Memory Access with Entity Supervision**Thibault Févry, Livio Baldini Soares, Nicholas FitzGerald, et al.*2020 · 205 citations*](https://md.research.elamur.ai/free/en/papers/fevry-2020-entities-as-experts-sparse-memory/)
377. [**Internet-augmented language models through few-shot prompting for open-domain question answering**Angeliki Lazaridou, Elena Gribovskaya, Wojciech Stokowiec, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/lazaridou-2022-internet-augmented-language-models-through/)
378. [**PRIMERA: Pyramid-based Masked Sentence Pre-training for Multi-document Summarization**Wen Xiao, Iz Beltagy, Giuseppe Carenini, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/xiao-2022-primera-pyramid-based-masked-sentence/)
379. [**Towards a Unified Multi-Dimensional Evaluator for Text Generation**Ming Zhong, Yang Liu, Da Yin, et al.*2022 · 204 citations*](https://md.research.elamur.ai/free/en/papers/zhong-2022-unified-multi-dimensional-evaluator-text/)
380. [**How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMs**Yi Zeng, Hongpeng Lin, Jingwen Zhang, et al.*2024 · 202 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2024-how-johnny-can-persuade-llms/)
381. [**TinyStories: How Small Can Language Models Be and Still Speak Coherent English?**Ronen Eldan, Yuanzhi Li*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/eldan-2023-tinystories-small-language-models-still/)
382. [**Multi-View Sequence-to-Sequence Models with Conversational Structure for Abstractive Dialogue Summarization**Jiaao Chen, Diyi Yang*2020 · 202 citations*](https://md.research.elamur.ai/free/en/papers/chen-2020-multi-view-sequence-sequence-models/)
383. [**SODA: Million-scale Dialogue Distillation with Social Commonsense Contextualization**Hyunwoo Kim, Jack Hessel, Liwei Jiang, et al.*2023 · 201 citations*](https://md.research.elamur.ai/free/en/papers/kim-2023-soda-million-scale-dialogue-distillation/)
384. [**AraT5: Text-to-Text Transformers for Arabic Language Generation**El Moatez Billah Nagoudi, AbdelRahim Elmadany, Muhammad Abdul-Mageed*2022 · 201 citations*](https://md.research.elamur.ai/free/en/papers/nagoudi-2022-arat5-text-text-transformers-arabic/)
385. [**Recursively Summarizing Books with Human Feedback**Jeff Wu, Long Ouyang, Daniel M. Ziegler, et al.*2021 · 201 citations*](https://md.research.elamur.ai/free/en/papers/wu-2021-recursively-summarizing-books-human-feedback/)
386. [**A Systematic Study and Comprehensive Evaluation of ChatGPT on Benchmark Datasets**Md Tahmid Rahman Laskar, M Saiful Bari, Mizanur Rahman, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/laskar-2023-systematic-study-comprehensive-evaluation-chatgpt/)
387. [**Measuring Attribution in Natural Language Generation Models**Hannah Rashkin, Vitaly Nikolaev, Matthew Lamm, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/rashkin-2023-measuring-attribution-natural-language-generation/)
388. [**MInference 1.0: Accelerating Pre-filling for Long-Context LLMs via Dynamic Sparse Attention**Huiqiang Jiang, Yucheng Li, Chengruidong Zhang, et al.*2024 · 197 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-minference-1-0-accelerating-pre/)
389. [**Scalable Extraction of Training Data from (Production) Language Models**Milad Nasr, Nicholas Carlini, Jonathan Hayase, et al.*2023 · 197 citations*](https://md.research.elamur.ai/free/en/papers/nasr-2023-scalable-extraction-training-data-production/)
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