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# 🧠 Language models & pretraining

1,079 papers · page 4 of 12 · 226–325 · the most cited first · [all topics](https://md.research.elamur.ai/free/en/topics/)

Models that learn a language from raw text, and how they are pretrained, tuned and prompted.

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226. [**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/)
227. [**Video-MME: The First-Ever Comprehensive Evaluation Benchmark of Multi-modal LLMs in Video Analysis**Chaoyou Fu, Yuhan Dai, Yongdong Luo, et al.*2024 · 403 citations*](https://md.research.elamur.ai/free/en/papers/fu-2024-video-mme-first-ever-comprehensive/)
228. [**True Few-Shot Learning with Language Models**Ethan Perez, Douwe Kiela, Kyunghyun Cho*2021 · 403 citations*](https://md.research.elamur.ai/free/en/papers/perez-2021-true-few-shot-learning-language/)
229. [**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/)
230. [**FlexGen: High-Throughput Generative Inference of Large Language Models with a Single GPU**Ying Sheng, Lianmin Zheng, Binhang Yuan, et al.*2023 · 399 citations*](https://md.research.elamur.ai/free/en/papers/sheng-2023-flexgen-high-throughput-generative-inference/)
231. [**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/)
232. [**DIN-SQL: Decomposed In-Context Learning of Text-to-SQL with Self-Correction**Mohammadreza Pourreza, Davood Rafiei*2023 · 397 citations*](https://md.research.elamur.ai/free/en/papers/pourreza-2023-din-sql-decomposed-context-learning/)
233. [**Large Language Models Cannot Self-Correct Reasoning Yet**Jie Huang, Xinyun Chen, Swaroop Mishra, et al.*2023 · 396 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-large-language-models-cannot-self/)
234. [**Whose Opinions Do Language Models Reflect?**Shibani Santurkar, Esin Durmus, Faisal Ladhak, et al.*2023 · 390 citations*](https://md.research.elamur.ai/free/en/papers/santurkar-2023-whose-opinions-language-models-reflect/)
235. [**Large Language Models as Optimizers**Chengrun Yang, Xuezhi Wang, Yifeng Lu, et al.*2023 · 388 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-large-language-models-optimizers/)
236. [**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/)
237. [**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/)
238. [**Immediate-Head Parsing for Language Models**Eugene Charniak*2001 · 386 citations*](https://md.research.elamur.ai/free/en/papers/charniak-2001-immediate-head-parsing-language-models/)
239. [**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/)
240. [**Query Rewriting in Retrieval-Augmented Large Language Models**Xinbei Ma, Yeyun Gong, Pengcheng He, et al.*2023 · 383 citations*](https://md.research.elamur.ai/free/en/papers/ma-2023-query-rewriting-retrieval-augmented-large/)
241. [**Enhancing Chat Language Models by Scaling High-quality Instructional Conversations**Ning Ding, Yulin Chen, Bokai Xu, et al.*2023 · 382 citations*](https://md.research.elamur.ai/free/en/papers/ding-2023-enhancing-chat-language-models-scaling/)
242. [**Gender and Representation Bias in GPT-3 Generated Stories**Li Lucy, David Bamman*2021 · 382 citations*](https://md.research.elamur.ai/free/en/papers/lucy-2021-gender-representation-bias-gpt-3/)
243. [**A Survey on Model Compression for Large Language Models**Xunyu Zhu, Jian Li, Yong Liu, et al.*2024 · 381 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-survey-model-compression-large-language/)
244. [**Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexity**Soyeong Jeong, Jinheon Baek, Sukmin Cho, et al.*2024 · 381 citations*](https://md.research.elamur.ai/free/en/papers/jeong-2024-adaptive-rag-learning-adapt-retrieval/)
245. [**Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy**Zhihong Shao, Yeyun Gong, Yelong Shen, et al.*2023 · 381 citations*](https://md.research.elamur.ai/free/en/papers/shao-2023-enhancing-retrieval-augmented-large-language/)
246. [**UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training**Hangbo Bao, Li Dong, Furu Wei, et al.*2020 · 380 citations*](https://md.research.elamur.ai/free/en/papers/bao-2020-unilmv2-pseudo-masked-language-models/)
247. [**Tulu 3: Pushing Frontiers in Open Language Model Post-Training**Nathan Lambert, Jacob Morrison, Valentina Pyatkin, et al.*2024 · 379 citations*](https://md.research.elamur.ai/free/en/papers/lambert-2024-tulu-3-pushing-frontiers-open/)
248. [**Emerging Cross-lingual Structure in Pretrained Language Models**Alexis Conneau, Shijie Wu, Haoran Li, et al.*2020 · 378 citations*](https://md.research.elamur.ai/free/en/papers/conneau-2020-emerging-cross-lingual-structure-pretrained/)
249. [**Tool Learning with Foundation Models**Yujia Qin, Shengding Hu, Yankai Lin, et al.*2023 · 376 citations*](https://md.research.elamur.ai/free/en/papers/qin-2023-tool-learning-foundation-models/)
250. [**A Watermark for Large Language Models**John Kirchenbauer, Jonas Geiping, Yuxin Wen, et al.*2023 · 374 citations*](https://md.research.elamur.ai/free/en/papers/kirchenbauer-2023-watermark-large-language-models/)
251. [**CogVLM: Visual Expert for Pretrained Language Models**Weihan Wang, Qingsong Lv, Wenmeng Yu, et al.*2023 · 373 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-cogvlm-visual-expert-pretrained/)
252. [**A Systematic Survey of Prompt Engineering in Large Language Models: Techniques and Applications**Pranab Sahoo; Ayush Kumar Singh; Sriparna Saha; Vinija Jain; Samrat Mondal;, Aman Chadha*2024 · 371 citations*](https://md.research.elamur.ai/free/en/papers/sahoo-2024-systematic-survey-prompt-engineering-large/)
253. [**Logic-LM: Empowering Large Language Models with Symbolic Solvers for Faithful Logical Reasoning**Liangming Pan, Alon Albalak, Xinyi Wang, et al.*2023 · 370 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-logic-lm-empowering-large-language/)
254. [**Large Language Models Are Reasoning Teachers**Namgyu Ho, Laura Schmid, Se-Young Yun*2023 · 368 citations*](https://md.research.elamur.ai/free/en/papers/ho-2023-large-language-models-are-reasoning/)
255. [**VILA: On Pre-training for Visual Language Models**Ji Lin, Hongxu Yin, Wei Ping, et al.*2023 · 367 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-vila-pre-training-visual-language/)
256. [**A Survey on Large Language Models for Code Generation**Juyong Jiang, Fan Wang, Jiasi Shen, et al.*2024 · 366 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2024-survey-large-language-models-code/)
257. [**WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct**Haipeng Luo, Qingfeng Sun, Can Xu, et al.*2023 · 366 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-wizardmath-empowering-mathematical-reasoning-large/)
258. [**Pre-training is a Hot Topic: Contextualized Document Embeddings Improve Topic Coherence**Federico Bianchi, Silvia Terragni, Dirk Hovy*2021 · 365 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2021-pre-training-is-hot-topic/)
259. [**The Interplay of Variant, Size, and Task Type in Arabic Pre-trained Language Models**Go Inoue, Bashar Alhafni, Nurpeiis Baimukan, et al.*2021 · 365 citations*](https://md.research.elamur.ai/free/en/papers/inoue-2021-interplay-variant-size-task-type/)
260. [**Identifying and Reducing Gender Bias in Word-Level Language Models**Shikha Bordia, Samuel R. Bowman*2019 · 363 citations*](https://md.research.elamur.ai/free/en/papers/bordia-2019-identifying-reducing-gender-bias-word/)
261. [**Augmented Language Models: a Survey**Grégoire Mialon, Roberto Dessì, Maria Lomeli, et al.*2023 · 362 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-augmented-language-models-survey/)
262. [**TOD-BERT: Pre-trained Natural Language Understanding for Task-Oriented Dialogue**Chien-Sheng Wu, Steven C.H. Hoi, Richard Socher, et al.*2020 · 360 citations*](https://md.research.elamur.ai/free/en/papers/wu-2020-tod-bert-pre-trained-natural/)
263. [**Orca: Progressive Learning from Complex Explanation Traces of GPT-4**Subhabrata Mukherjee, Arindam Mitra, Ganesh Jawahar, et al.*2023 · 357 citations*](https://md.research.elamur.ai/free/en/papers/mukherjee-2023-orca-progressive-learning-complex-explanation/)
264. [**Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor**Or Honovich, Thomas Scialom, Omer Levy, et al.*2023 · 356 citations*](https://md.research.elamur.ai/free/en/papers/honovich-2023-unnatural-instructions-tuning-language-models/)
265. [**XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation**Yaobo Liang, Nan Duan, Yeyun Gong, et al.*2020 · 356 citations*](https://md.research.elamur.ai/free/en/papers/liang-2020-xglue-new-benchmark-dataset-cross/)
266. [**Multilingual Constituency Parsing with Self-Attention and Pre-Training**Nikita Kitaev, Steven Cao, Dan Klein*2019 · 356 citations*](https://md.research.elamur.ai/free/en/papers/kitaev-2019-multilingual-constituency-parsing-self-attention/)
267. [**Synthetic QA Corpora Generation with Roundtrip Consistency**Chris Alberti, Daniel Andor, Emily Pitler, et al.*2019 · 355 citations*](https://md.research.elamur.ai/free/en/papers/alberti-2019-synthetic-qa-corpora-generation-roundtrip/)
268. [**Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback**Baolin Peng, Michel Galley, Pengcheng He, et al.*2023 · 354 citations*](https://md.research.elamur.ai/free/en/papers/peng-2023-check-facts-try-again-improving/)
269. [**Towards Understanding Sycophancy in Language Models**Mrinank Sharma, Meg Tong, Tomasz Korbak, et al.*2023 · 354 citations*](https://md.research.elamur.ai/free/en/papers/sharma-2023-towards-understanding-sycophancy-language-models/)
270. [**Towards Mitigating LLM Hallucination via Self Reflection**Ziwei Ji, Tiezheng Yu, Yan Xu, et al.*2023 · 352 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-mitigating-llm-hallucination-self-reflection/)
271. [**Large Language Models Struggle to Learn Long-Tail Knowledge**Nikhil Kandpal, Haikang Deng, Adam Roberts, et al.*2022 · 352 citations*](https://md.research.elamur.ai/free/en/papers/kandpal-2022-large-language-models-struggle-learn/)
272. [**BeaverTails: Towards Improved Safety Alignment of LLM via a Human-Preference Dataset**Jiaming Ji, Mickel Liu, Juntao Dai, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/ji-2023-beavertails-towards-improved-safety-alignment/)
273. [**RARR: Researching and Revising What Language Models Say, Using Language Models**Luyu Gao, Zhuyun Dai, Panupong Pasupat, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/gao-2023-rarr-researching-revising-what-language/)
274. [**SnapKV: LLM Knows What You are Looking for Before Generation**Yuhong Li, Yingbing Huang, Bowen Yang, et al.*2024 · 350 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-snapkv-llm-knows-looking-before/)
275. [**TinyLlama: An Open-Source Small Language Model**Peiyuan Zhang, Guangtao Zeng, Tianduo Wang, et al.*2024 · 350 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-tinyllama-open-source-small-language/)
276. [**SpeechGPT: Empowering Large Language Models with Intrinsic Cross-Modal Conversational Abilities**Dong Zhang, Shimin Li, Xin Zhang, et al.*2023 · 346 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-speechgpt-empowering-large-language-models/)
277. [**RobBERT: a Dutch RoBERTa-based Language Model**Pieter Delobelle, Thomas Winters, Bettina Berendt*2020 · 346 citations*](https://md.research.elamur.ai/free/en/papers/delobelle-2020-robbert-dutch-roberta-based-language/)
278. [**Toxicity in chatgpt: Analyzing persona-assigned language models**Ameet Deshpande, Vishvak Murahari, Tanmay Rajpurohit, et al.*2023 · 344 citations*](https://md.research.elamur.ai/free/en/papers/deshpande-2023-toxicity-chatgpt-analyzing-persona-assigned/)
279. [**Complexity-Based Prompting for Multi-Step Reasoning**Yao Fu, Hao Peng, Ashish Sabharwal, et al.*2022 · 343 citations*](https://md.research.elamur.ai/free/en/papers/fu-2022-complexity-based-prompting-multi-step/)
280. [**Symbolic Knowledge Distillation: from General Language Models to Commonsense Models**Peter West, Chandra Bhagavatula, Jack Hessel, et al.*2022 · 343 citations*](https://md.research.elamur.ai/free/en/papers/west-2022-symbolic-knowledge-distillation-general-language/)
281. [**Teaching Small Language Models to Reason**Lucie Charlotte Magister, Jonathan Mallinson, Jakub Adamek, et al.*2023 · 340 citations*](https://md.research.elamur.ai/free/en/papers/magister-2023-teaching-small-language-models-reason/)
282. [**Large Language Models for Information Retrieval: A Survey**Yutao Zhu; Huaying Yuan; Shuting Wang; Jiongnan Liu; Wenhan Liu; Chenlong Deng; Haonan Chen; Zheng Liu; Zhicheng Dou;, Ji-Rong Wen*2023 · 335 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-large-language-models-information-retrieval/)
283. [**LLMLingua: Compressing Prompts for Accelerated Inference of Large Language Models**Huiqiang Jiang, Qianhui Wu, Chin-Yew Lin, et al.*2023 · 335 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-llmlingua-compressing-prompts-accelerated-inference/)
284. [**UL2: Unifying Language Learning Paradigms**Yi Tay, Mostafa Dehghani, Vinh Q. Tran, et al.*2022 · 334 citations*](https://md.research.elamur.ai/free/en/papers/tay-2022-ul2-unifying-language-learning-paradigms/)
285. [**DeepSeek-V3.2: Pushing the Frontier of Open Large Language Models**DeepSeek-AI, Aixin Liu, Aoxue Mei, et al.*2025 · 333 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2025-deepseek-v3-2-pushing/)
286. [**AutoDAN: Generating Stealthy Jailbreak Prompts on Aligned Large Language Models**Xiaogeng Liu, Nan Xu, Muhao Chen, et al.*2023 · 333 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-autodan-generating-stealthy-jailbreak-prompts/)
287. [**Neural Codec Language Models are Zero-Shot Text to Speech Synthesizers**Chengyi Wang, Sanyuan Chen, Yu Wu, et al.*2023 · 333 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-neural-codec-language-models-zero/)
288. [**Fine-tuning Aligned Language Models Compromises Safety, Even When Users Do Not Intend To!**Xiangyu Qi, Yi Zeng, Tinghao Xie, et al.*2023 · 331 citations*](https://md.research.elamur.ai/free/en/papers/qi-2023-fine-tuning-aligned-language-models/)
289. [**TimeLMs: Diachronic Language Models from Twitter**Daniel Loureiro, Francesco Barbieri, Leonardo Neves, et al.*2022 · 331 citations*](https://md.research.elamur.ai/free/en/papers/loureiro-2022-timelms-diachronic-language-models-twitter/)
290. [**Unsupervised Corpus Aware Language Model Pre-training for Dense Passage Retrieval**Luyu Gao, Jamie Callan*2022 · 328 citations*](https://md.research.elamur.ai/free/en/papers/gao-2022-unsupervised-corpus-aware-language-model/)
291. [**GraphCodeBERT: Pre-training Code Representations with Data Flow**Daya Guo, Shuo Ren, Shuai Lu, et al.*2021 · 328 citations*](https://md.research.elamur.ai/free/en/papers/guo-2021-graphcodebert-pre-training-code-representations/)
292. [**HallusionBench: An Advanced Diagnostic Suite for Entangled Language Hallucination and Visual Illusion in Large Vision-Language Models**Tianrui Guan, Fuxiao Liu, Xiyang Wu, et al.*2023 · 326 citations*](https://md.research.elamur.ai/free/en/papers/guan-2023-hallusionbench-advanced-diagnostic-suite-entangled/)
293. [**Unsupervised Domain Clusters in Pretrained Language Models**Roee Aharoni, Yoav Goldberg*2020 · 326 citations*](https://md.research.elamur.ai/free/en/papers/aharoni-2020-unsupervised-domain-clusters-pretrained-language/)
294. [**ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation**Yu Sun, Shuohuan Wang, Shikun Feng, et al.*2021 · 324 citations*](https://md.research.elamur.ai/free/en/papers/sun-2021-ernie-3-0-large/)
295. [**Are Emergent Abilities of Large Language Models a Mirage?**Rylan Schaeffer, Brando Miranda, Sanmi Koyejo*2023 · 323 citations*](https://md.research.elamur.ai/free/en/papers/schaeffer-2023-emergent-abilities-large-language-models/)
296. [**LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention**Renrui Zhang, Jiaming Han, Chris Liu, et al.*2023 · 322 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-llama-adapter-efficient-fine-tuning/)
297. [**"Do Anything Now": Characterizing and Evaluating In-The-Wild Jailbreak Prompts on Large Language Models**Xinyue Shen, Zeyuan Chen, Michael Backes, et al.*2023 · 320 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-anything-now-characterizing-evaluating-wild/)
298. [**Probing Pretrained Language Models for Lexical Semantics**Ivan Vulić, Edoardo Maria Ponti, Robert Litschko, et al.*2020 · 320 citations*](https://md.research.elamur.ai/free/en/papers/vulic-2020-probing-pretrained-language-models-lexical/)
299. [**Language Is Not All You Need: Aligning Perception with Language Models**Shaohan Huang, Li Dong, Wenhui Wang, et al.*2023 · 319 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-language-all-need-aligning-perception/)
300. [**Mitigating Object Hallucinations in Large Vision-Language Models through Visual Contrastive Decoding**Sicong Leng, Hang Zhang, Guanzheng Chen, et al.*2023 · 317 citations*](https://md.research.elamur.ai/free/en/papers/leng-2023-mitigating-object-hallucinations-large-vision/)
301. [**Text and Code Embeddings by Contrastive Pre-Training**Arvind Neelakantan, Tao Xu, Raul Puri, et al.*2022 · 316 citations*](https://md.research.elamur.ai/free/en/papers/neelakantan-2022-text-code-embeddings-contrastive-pre/)
302. [**CRITIC: Large Language Models Can Self-Correct with Tool-Interactive Critiquing**Zhibin Gou, Zhihong Shao, Yeyun Gong, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/gou-2023-critic-large-language-models-self/)
303. [**Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Models**Chenfei Wu, Shengming Yin, Weizhen Qi, et al.*2023 · 315 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-visual-chatgpt-talking-drawing-editing/)
304. [**LLM-Blender: Ensembling Large Language Models with Pairwise Ranking and Generative Fusion**Dongfu Jiang, Xiang Ren, Bill Yuchen Lin*2023 · 314 citations*](https://md.research.elamur.ai/free/en/papers/jiang-2023-llm-blender-ensembling-large-language/)
305. [**Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models**Yanzhao Zhang, Mingxin Li, Dingkun Long, et al.*2025 · 313 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2025-qwen3-embedding-advancing-text/)
306. [**Unsupervised Pretraining for Sequence to Sequence Learning**Prajit Ramachandran, Peter Liu, Quoc Le*2017 · 312 citations*](https://md.research.elamur.ai/free/en/papers/ramachandran-2017-unsupervised-pretraining-sequence-sequence-learning/)
307. [**Hallucination is Inevitable: An Innate Limitation of Large Language Models**Ziwei Xu, Sanjay Jain, Mohan Kankanhalli*2024 · 309 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-hallucination-inevitable-innate-limitation-large/)
308. [**From Pretraining Data to Language Models to Downstream Tasks: Tracking the Trails of Political Biases Leading to Unfair NLP Models**Shangbin Feng, Chan Young Park, Yuhan Liu, et al.*2023 · 305 citations*](https://md.research.elamur.ai/free/en/papers/feng-2023-pretraining-data-language-models-downstream/)
309. [**Discovering Language Model Behaviors with Model-Written Evaluations**Ethan Perez, Sam Ringer, Kamilė Lukošiūtė, et al.*2022 · 305 citations*](https://md.research.elamur.ai/free/en/papers/perez-2022-discovering-language-model-behaviors-model/)
310. [**How Far Can Camels Go? Exploring the State of Instruction Tuning on Open Resources**Yizhong Wang, Hamish Ivison, Pradeep Dasigi, et al.*2023 · 304 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-far-camels-go-exploring-state/)
311. [**Search-R1: Training LLMs to Reason and Leverage Search Engines with Reinforcement Learning**Bowen Jin, Hansi Zeng, Zhenrui Yue, et al.*2025 · 303 citations*](https://md.research.elamur.ai/free/en/papers/jin-2025-search-r1-training-llms-reason/)
312. [**The Dawn of LMMs: Preliminary Explorations with GPT-4V(ision)**Zhengyuan Yang, Linjie Li, Kevin Lin, et al.*2023 · 303 citations*](https://md.research.elamur.ai/free/en/papers/yang-2023-dawn-lmms-preliminary-explorations-gpt/)
313. [**Large Language Models are Better Reasoners with Self-Verification**Yixuan Weng, Minjun Zhu, Fei Xia, et al.*2023 · 302 citations*](https://md.research.elamur.ai/free/en/papers/weng-2023-large-language-models-are-better/)
314. [**PromptRobust: Towards Evaluating the Robustness of Large Language Models on Adversarial Prompts**Kaijie Zhu, Jindong Wang, Jiaheng Zhou, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-promptrobust-towards-evaluating-robustness-large/)
315. [**Challenges and Applications of Large Language Models**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 300 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
316. [**Want To Reduce Labeling Cost? GPT-3 Can Help**Shuohang Wang, Yang Liu, Yichong Xu, et al.*2021 · 298 citations*](https://md.research.elamur.ai/free/en/papers/wang-2021-want-reduce-labeling-cost-gpt/)
317. [**Eyes Wide Shut? Exploring the Visual Shortcomings of Multimodal LLMs**Shengbang Tong, Zhuang Liu, Yuexiang Zhai, et al.*2024 · 297 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-eyes-wide-shut-exploring-visual/)
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