---
title: "Who cites Yanqi Zhou — page 2"
description: "8,127 papers of the Elamur Research library cite the works of Yanqi Zhou, found in their bibliographies · page 2 of 7 · 101–200; 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 2 of 7 · 101–200 · [← Yanqi Zhou](https://md.research.elamur.ai/free/en/authors/yanqi-zhou/)

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101. [**Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond**Jingfeng Yang, Hongye Jin, Ruixiang Tang, et al.*2023 · 806 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-harnessing-power-llms-practice-survey/)
102. [**BARTScore: Evaluating Generated Text as Text Generation**Weizhe Yuan, Graham Neubig, Pengfei Liu*2021 · 806 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2021-bartscore-evaluating-generated-text-text/)
103. [**XLS-R: Self-supervised Cross-lingual Speech Representation Learning at Scale**Arun Babu, Changhan Wang, Andros Tjandra, et al.*2021 · 799 citations*](https://md.research.elamur.ai/free/en/papers/babu-2021-xls-r-self-supervised-cross/)
104. [**Explainability for Large Language Models: A Survey**Haiyan Zhao, Hanjie Chen, Fan Yang, et al.*2023 · 785 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2023-explainability-large-language-models-survey/)
105. [**Can Large Language Models Transform Computational Social Science?**Caleb Ziems, William Held, Omar Shaikh, et al.*2023 · 776 citations*](https://md.research.elamur.ai/free/en/papers/ziems-2023-large-language-models-transform-computational/)
106. [**Towards Reasoning in Large Language Models: A Survey**Jie Huang, Kevin Chen-Chuan Chang*2023 · 775 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-reasoning-large-language-models-survey/)
107. [**GLM-130B: An Open Bilingual Pre-trained Model**Aohan Zeng, Xiao Liu, Zhengxiao Du, et al.*2022 · 775 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-glm-130b-open-bilingual-pre/)
108. [**Reproducible scaling laws for contrastive language-image learning**Mehdi Cherti, Romain Beaumont, Ross Wightman, et al.*2022 · 750 citations*](https://md.research.elamur.ai/free/en/papers/cherti-2022-reproducible-scaling-laws-contrastive-language/)
109. [**AdapterHub: A Framework for Adapting Transformers**Jonas Pfeiffer, Andreas Rücklé, Clifton Poth, et al.*2020 · 747 citations*](https://md.research.elamur.ai/free/en/papers/pfeiffer-2020-adapterhub-framework-adapting-transformers/)
110. [**ChartQA: A Benchmark for Question Answering about Charts with Visual and Logical Reasoning**Ahmed Masry, Do Xuan Long, Jia Qing Tan, et al.*2022 · 744 citations*](https://md.research.elamur.ai/free/en/papers/masry-2022-chartqa-benchmark-question-answering-about/)
111. [**STaR: Bootstrapping Reasoning With Reasoning**Eric Zelikman, Yuhuai Wu, Jesse Mu, et al.*2022 · 743 citations*](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/)
112. [**Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 735 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/)
113. [**IndicNLPSuite: Monolingual Corpora, Evaluation Benchmarks and Pre-trained Multilingual Language Models for Indian Languages**Divyanshu Kakwani, Anoop Kunchukuttan, Satish Golla, et al.*2020 · 730 citations*](https://md.research.elamur.ai/free/en/papers/kakwani-2020-indicnlpsuite-monolingual-corpora-evaluation-benchmarks/)
114. [**WizardLM: Empowering large pre-trained language models to follow complex instructions**Can Xu, Qingfeng Sun, Kai Zheng, et al.*2023 · 729 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-wizardlm-empowering-large-pre-trained/)
115. [**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/)
116. [**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/)
117. [**CAMEL: Communicative Agents for "Mind" Exploration of Large Language Model Society**Guohao Li, Hasan Abed Al Kader Hammoud, Hani Itani, et al.*2023 · 689 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-camel-communicative-agents-mind/)
118. [**Automatic Chain of Thought Prompting in Large Language Models**Zhuosheng Zhang, Aston Zhang, Mu Li, et al.*2022 · 688 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2022-automatic-chain-thought-prompting-large/)
119. [**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/)
120. [**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/)
121. [**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/)
122. [**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/)
123. [**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/)
124. [**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/)
125. [**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/)
126. [**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/)
127. [**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/)
128. [**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/)
129. [**Role-Play with Large Language Models**Murray Shanahan, Kyle McDonell, Laria Reynolds*2023 · 606 citations*](https://md.research.elamur.ai/free/en/papers/shanahan-2023-role-play-large-language-models/)
130. [**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/)
131. [**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/)
132. [**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/)
133. [**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/)
134. [**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/)
135. [**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/)
136. [**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/)
137. [**Improving Factuality and Reasoning in Language Models through Multiagent Debate**Yilun Du, Shuang Li, Antonio Torralba, et al.*2023 · 564 citations*](https://md.research.elamur.ai/free/en/papers/du-2023-improving-factuality-reasoning-language-models/)
138. [**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/)
139. [**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/)
140. [**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/)
141. [**Precise Zero-Shot Dense Retrieval without Relevance Labels**Luyu Gao, Xueguang Ma, Jimmy Lin, et al.*2022 · 545 citations*](https://md.research.elamur.ai/free/en/papers/gao-2022-precise-zero-shot-dense-retrieval/)
142. [**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/)
143. [**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/)
144. [**Gorilla: Large Language Model Connected with Massive APIs**Shishir G. Patil, Tianjun Zhang, Xin Wang, et al.*2023 · 527 citations*](https://md.research.elamur.ai/free/en/papers/patil-2023-gorilla-large-language-model-connected/)
145. [**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/)
146. [**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/)
147. [**SWE-agent: Agent-Computer Interfaces Enable Automated Software Engineering**John Yang, Carlos E. Jimenez, Alexander Wettig, et al.*2024 · 507 citations*](https://md.research.elamur.ai/free/en/papers/yang-2024-swe-agent-agent-computer-interfaces/)
148. [**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/)
149. [**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/)
150. [**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/)
151. [**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/)
152. [**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/)
153. [**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/)
154. [**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/)
155. [**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/)
156. [**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/)
157. [**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/)
158. [**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/)
159. [**Large Language Models: A Survey**Shervin Minaee, Tomas Mikolov, Narjes Nikzad, et al.*2024 · 467 citations*](https://md.research.elamur.ai/free/en/papers/minaee-2024-large-language-models-survey/)
160. [**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/)
161. [**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/)
162. [**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/)
163. [**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/)
164. [**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/)
165. [**Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned**Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.*2022 · 456 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/)
166. [**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/)
167. [**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/)
168. [**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/)
169. [**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/)
170. [**LLM-Pruner: On the Structural Pruning of Large Language Models**Xinyin Ma, Gongfan Fang, Xinchao Wang*2023 · 447 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-llm-pruner-structural-pruning-large/)
171. [**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/)
172. [**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/)
173. [**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/)
174. [**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/)
175. [**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/)
176. [**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/)
177. [**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/)
178. [**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/)
179. [**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/)
180. [**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/)
181. [**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/)
182. [**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/)
183. [**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/)
184. [**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/)
185. [**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/)
186. [**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/)
187. [**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/)
188. [**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/)
189. [**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/)
190. [**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/)
191. [**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/)
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