---
title: "Who cites Pamela Mishkin — page 3"
description: "8,739 papers of the Elamur Research library cite the works of Pamela Mishkin, found in their bibliographies · page 3 of 6 · 201–300; the most cited first."
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---
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# Who cites Pamela Mishkin

9,069 citations of their works from 8,739 papers of our library (of the 140,884 whose bibliographies we read) · 574 with a page here, the most cited first · page 3 of 6 · 201–300 · [← Pamela Mishkin](https://md.research.elamur.ai/free/en/authors/pamela-mishkin/)

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201. [**On-Policy Distillation of Language Models: Learning from Self-Generated Mistakes**Rishabh Agarwal, Nino Vieillard, Yongchao Zhou, et al.*2023 · 288 citations*](https://md.research.elamur.ai/free/en/papers/agarwal-2023-policy-distillation-language-models-learning/)
202. [**Can language models learn from explanations in context?**Andrew Lampinen, Ishita Dasgupta, Stephanie Chan, et al.*2022 · 288 citations*](https://md.research.elamur.ai/free/en/papers/lampinen-2022-can-language-models-learn-explanations/)
203. [**Efficient and Effective Text Encoding for Chinese LLaMA and Alpaca**Yiming Cui, Ziqing Yang, Xin Yao*2023 · 285 citations*](https://md.research.elamur.ai/free/en/papers/cui-2023-efficient-effective-text-encoding-chinese/)
204. [**Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language**Andy Zeng, Maria Attarian, Brian Ichter, et al.*2022 · 285 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/)
205. [**Is ChatGPT a Good NLG Evaluator? A Preliminary Study**Jiaan Wang, Yunlong Liang, Fandong Meng, et al.*2023 · 281 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-chatgpt-good-nlg-evaluator-preliminary/)
206. [**MEGA: Multilingual Evaluation of Generative AI**Kabir Ahuja, Harshita Diddee, Rishav Hada, et al.*2023 · 281 citations*](https://md.research.elamur.ai/free/en/papers/ahuja-2023-mega-multilingual-evaluation-generative-ai/)
207. [**Generate rather than Retrieve: Large Language Models are Strong Context Generators**Wenhao Yu, Dan Iter, Shuohang Wang, et al.*2022 · 280 citations*](https://md.research.elamur.ai/free/en/papers/yu-2022-generate-rather-than-retrieve-large/)
208. [**Large Language Models for Mathematical Reasoning: Progresses and Challenges**Janice Ahn, Rishu Verma, Renze Lou, et al.*2024 · 279 citations*](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/)
209. [**HuatuoGPT, Towards Taming Language Model to Be a Doctor**Hongbo Zhang, Junying Chen, Feng Jiang, et al.*2023 · 279 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-huatuogpt-taming-language-model-be/)
210. [**Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study**Yong Cao, Li Zhou, Seolhwa Lee, et al.*2023 · 278 citations*](https://md.research.elamur.ai/free/en/papers/cao-2023-assessing-cross-cultural-alignment-between/)
211. [**Larger language models do in-context learning differently**Jerry Wei, Jason Wei, Yi Tay, et al.*2023 · 277 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-larger-language-models-context-learning/)
212. [**A Survey on LLM-Generated Text Detection: Necessity, Methods, and Future Directions**Junchao Wu, Shu Yang, Runzhe Zhan, et al.*2025 · 276 citations*](https://md.research.elamur.ai/free/en/papers/wu-2025-survey-llm-generated-text-detection/)
213. [**An Image is Worth 1/2 Tokens After Layer 2: Plug-and-Play Inference Acceleration for Large Vision-Language Models**Liang Chen, Haozhe Zhao, Tianyu Liu, et al.*2024 · 276 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-image-worth-1-2-tokens/)
214. [**Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling**Zhe Chen, Weiyun Wang, Yue Cao, et al.*2024 · 276 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/)
215. [**LLM-QAT: Data-Free Quantization Aware Training for Large Language Models**Zechun Liu, Barlas Oguz, Changsheng Zhao, et al.*2024 · 275 citations*](https://md.research.elamur.ai/free/en/papers/liu-2024-llm-qat-data-free-quantization/)
216. [**Llemma: An Open Language Model For Mathematics**Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, et al.*2023 · 275 citations*](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-llemma-open-language-model-mathematics/)
217. [**JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models**Patrick Chao, Edoardo Debenedetti, Alexander Robey, et al.*2024 · 273 citations*](https://md.research.elamur.ai/free/en/papers/chao-2024-jailbreakbench-open-robustness-benchmark-jailbreaking/)
218. [**ChatIE: Zero-Shot Information Extraction via Chatting with ChatGPT**Xiang Wei; Xingyu Cui; Ning Cheng; Xiaobin Wang; Xin Zhang; Shen Huang; Pengjun Xie; Jinan Xu; Yufeng Chen; Meishan Zhang; Yong Jiang;, Wenjuan Han*2023 · 272 citations*](https://md.research.elamur.ai/free/en/papers/wei-2023-chatie-zero-shot-information-extraction/)
219. [**Corrective Retrieval Augmented Generation**Shi-Qi Yan, Jia-Chen Gu, Yun Zhu, et al.*2024 · 271 citations*](https://md.research.elamur.ai/free/en/papers/yan-2024-corrective-retrieval-augmented-generation/)
220. [**Medusa: Simple LLM Inference Acceleration Framework with Multiple Decoding Heads**Tianle Cai, Yuhong Li, Zhengyang Geng, et al.*2024 · 271 citations*](https://md.research.elamur.ai/free/en/papers/cai-2024-medusa-simple-llm-inference-acceleration/)
221. [**Towards Understanding Chain-of-Thought Prompting: An Empirical Study of What Matters**Boshi Wang, Sewon Min, Xiang Deng, et al.*2023 · 267 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-understanding-chain-thought-prompting-empirical/)
222. [**Scaling Laws for Reward Model Overoptimization**Leo Gao, John Schulman, Jacob Hilton*2022 · 266 citations*](https://md.research.elamur.ai/free/en/papers/gao-2022-scaling-laws-reward-model-overoptimization/)
223. [**Are We on the Right Way for Evaluating Large Vision-Language Models?**Lin Chen, Jinsong Li, Xiaoyi Dong, et al.*2024 · 263 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-right-way-evaluating-large-vision/)
224. [**DoLa: Decoding by Contrasting Layers Improves Factuality in Large Language Models**Yung-Sung Chuang, Yujia Xie, Hongyin Luo, et al.*2023 · 261 citations*](https://md.research.elamur.ai/free/en/papers/chuang-2023-dola-decoding-contrasting-layers-improves/)
225. [**DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelines**Omar Khattab, Arnav Singhvi, Paridhi Maheshwari, et al.*2023 · 261 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2023-dspy-compiling-declarative-language-model/)
226. [**Large Language Models for Data Annotation and Synthesis: A Survey**Zhen Tan, Dawei Li, Song Wang, et al.*2024 · 259 citations*](https://md.research.elamur.ai/free/en/papers/tan-2024-large-language-models-data-annotation/)
227. [**Large Language Models Sensitivity to The Order of Options in Multiple-Choice Questions**Pouya Pezeshkpour, Estevam Hruschka*2023 · 256 citations*](https://md.research.elamur.ai/free/en/papers/pezeshkpour-2023-large-language-models-sensitivity-order/)
228. [**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/)
229. [**Personality Traits in Large Language Models**Greg Serapio-Garcia, Mustafa Safdari, Clement Crepy, et al.*2023 · 251 citations*](https://md.research.elamur.ai/free/en/papers/serapio-garcia-2023-personality-traits-large-language-models/)
230. [**A General Theoretical Paradigm to Understand Learning from Human Preferences**Mohammad Gheshlaghi Azar, Mark Rowland, Bilal Piot, et al.*2023 · 250 citations*](https://md.research.elamur.ai/free/en/papers/azar-2023-general-theoretical-paradigm-understand-learning/)
231. [**The Curse of Recursion: Training on Generated Data Makes Models Forget**Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, et al.*2023 · 250 citations*](https://md.research.elamur.ai/free/en/papers/shumailov-2023-curse-recursion-training-generated-data/)
232. [**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/)
233. [**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/)
234. [**Hallucinations in Large Multilingual Translation Models**Nuno M. Guerreiro, Duarte M. Alves, Jonas Waldendorf, et al.*2023 · 244 citations*](https://md.research.elamur.ai/free/en/papers/guerreiro-2023-hallucinations-large-multilingual-translation-models/)
235. [**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/)
236. [**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/)
237. [**RAFT: Reward rAnked FineTuning for Generative Foundation Model Alignment**Hanze Dong, Wei Xiong, Deepanshu Goyal, et al.*2023 · 239 citations*](https://md.research.elamur.ai/free/en/papers/dong-2023-raft-reward-ranked-finetuning-generative/)
238. [**Language Models Are Greedy Reasoners: A Systematic Formal Analysis of Chain-of-Thought**Abulhair Saparov, He He*2022 · 238 citations*](https://md.research.elamur.ai/free/en/papers/saparov-2022-language-models-greedy-reasoners-systematic/)
239. [**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/)
240. [**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/)
241. [**Parameter-Efficient Fine-Tuning for Large Models: A Comprehensive Survey**Zeyu Han, Chao Gao, Jinyang Liu, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/han-2024-parameter-efficient-fine-tuning-large/)
242. [**Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models**Myra Cheng, Esin Durmus, Dan Jurafsky*2023 · 236 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2023-marked-personas-natural-language-prompts/)
243. [**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/)
244. [**SemEval-2022 Task 5: Multimedia Automatic Misogyny Identification**Elisabetta Fersini, Francesca Gasparini, Giulia Rizzi, et al.*2022 · 234 citations*](https://md.research.elamur.ai/free/en/papers/fersini-2022-semeval-2022-task-5-multimedia/)
245. [**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/)
246. [**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/)
247. [**MathVerse: Does Your Multi-modal LLM Truly See the Diagrams in Visual Math Problems?**Renrui Zhang, Dongzhi Jiang, Yichi Zhang, et al.*2024 · 231 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-mathverse-multi-modal-llm-truly/)
248. [**XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models**Paul Rottger, Hannah Rose Kirk, Bertie Vidgen, et al.*2023 · 227 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2023-xstest-test-suite-identifying-exaggerated/)
249. [**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/)
250. [**Baseline Defenses for Adversarial Attacks Against Aligned Language Models**Neel Jain, Avi Schwarzschild, Yuxin Wen, et al.*2023 · 224 citations*](https://md.research.elamur.ai/free/en/papers/jain-2023-baseline-defenses-adversarial-attacks-against/)
251. [**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/)
252. [**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/)
253. [**How Language Model Hallucinations Can Snowball**Muru Zhang, Ofir Press, William Merrill, et al.*2023 · 221 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-language-model-hallucinations-snowball/)
254. [**MedAlpaca — An Open-Source Collection of Medical Conversational AI Models and Training Data**Tianyu Han, Lisa C. Adams, Jens-Michalis Papaioannou, et al.*2023 · 221 citations*](https://md.research.elamur.ai/free/en/papers/han-2023-medalpaca-open-source-collection-medical/)
255. [**Not what you've signed up for: Compromising Real-World LLM-Integrated Applications with Indirect Prompt Injection**Kai Greshake, Sahar Abdelnabi, Shailesh Mishra, et al.*2023 · 220 citations*](https://md.research.elamur.ai/free/en/papers/greshake-2023-ve-signed-up-compromising-real/)
256. [**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/)
257. [**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/)
258. [**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/)
259. [**Self-critiquing models for assisting human evaluators**William Saunders, Catherine Yeh, Jeff Wu, et al.*2022 · 219 citations*](https://md.research.elamur.ai/free/en/papers/saunders-2022-self-critiquing-models-assisting-human/)
260. [**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/)
261. [**AlpaGasus: Training A Better Alpaca with Fewer Data**Lichang Chen, Shiyang Li, Jun Yan, et al.*2023 · 214 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-alpagasus-training-better-alpaca-fewer/)
262. [**WildChat: 1M ChatGPT Interaction Logs in the Wild**Wenting Zhao, Xiang Ren, Jack Hessel, et al.*2024 · 213 citations*](https://md.research.elamur.ai/free/en/papers/zhao-2024-wildchat-1m-chatgpt-interaction-logs/)
263. [**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/)
264. [**GAIA: a benchmark for General AI Assistants**Gregoire Mialon, Clementine Fourrier, Craig Swift, et al.*2023 · 211 citations*](https://md.research.elamur.ai/free/en/papers/mialon-2023-gaia-benchmark-general-ai-assistants/)
265. [**Demonstrate-Search-Predict: Composing retrieval and language models for knowledge-intensive NLP**Omar Khattab, Keshav Santhanam, Xiang Lisa Li, et al.*2022 · 211 citations*](https://md.research.elamur.ai/free/en/papers/khattab-2022-demonstrate-search-predict-composing-retrieval/)
266. [**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/)
267. [**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/)
268. [**Reinforced Self-Training (ReST) for Language Modeling**Caglar Gulcehre, Tom Le Paine, Srivatsan Srinivasan, et al.*2023 · 209 citations*](https://md.research.elamur.ai/free/en/papers/gulcehre-2023-reinforced-self-training-rest-language/)
269. [**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/)
270. [**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/)
271. [**Sources of Hallucination by Large Language Models on Inference Tasks**Nick McKenna, Tianyi Li, Liang Cheng, et al.*2023 · 208 citations*](https://md.research.elamur.ai/free/en/papers/mckenna-2023-sources-hallucination-large-language-models/)
272. [**Contrastive Preference Optimization: Pushing the Boundaries of LLM Performance in Machine Translation**Haoran Xu, Amr Sharaf, Yunmo Chen, et al.*2024 · 207 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-contrastive-preference-optimization-pushing-boundaries/)
273. [**Exploring Human-Like Translation Strategy with Large Language Models**Zhiwei He, Tian Liang, Wenxiang Jiao, et al.*2024 · 207 citations*](https://md.research.elamur.ai/free/en/papers/he-2024-exploring-human-like-translation-strategy/)
274. [**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/)
275. [**RAGTruth: A Hallucination Corpus for Developing Trustworthy Retrieval-Augmented Language Models**Cheng Niu, Yuanhao Wu, Juno Zhu, et al.*2024 · 204 citations*](https://md.research.elamur.ai/free/en/papers/niu-2024-ragtruth-hallucination-corpus-developing-trustworthy/)
276. [**SALMONN: Towards Generic Hearing Abilities for Large Language Models**Changli Tang, Wenyi Yu, Guangzhi Sun, et al.*2023 · 202 citations*](https://md.research.elamur.ai/free/en/papers/tang-2023-salmonn-towards-generic-hearing-abilities/)
277. [**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/)
278. [**IndicTrans2: Towards High-Quality and Accessible Machine Translation Models for all 22 Scheduled Indian Languages**Jay Gala, Pranjal A. Chitale, Raghavan AK, et al.*2023 · 200 citations*](https://md.research.elamur.ai/free/en/papers/gala-2023-indictrans2-towards-high-quality-accessible/)
279. [**Aligning Large Language Models with Human: A Survey**Yufei Wang, Wanjun Zhong, Liangyou Li, et al.*2023 · 199 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-aligning-large-language-models-human/)
280. [**Camels in a Changing Climate: Enhancing LM Adaptation with Tulu 2**Hamish Ivison, Yizhong Wang, Valentina Pyatkin, et al.*2023 · 199 citations*](https://md.research.elamur.ai/free/en/papers/ivison-2023-camels-changing-climate-enhancing-lm/)
281. [**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/)
282. [**Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias**Yue Yu, Yuchen Zhuang, Jieyu Zhang, et al.*2023 · 198 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-large-language-model-attributed-training/)
283. [**ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search**Dan Zhang, Sining Zhoubian, Ziniu Hu, et al.*2024 · 197 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-rest-mcts-llm-self-training/)
284. [**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/)
285. [**Does Reinforcement Learning Really Incentivize Reasoning Capacity in LLMs Beyond the Base Model?**Yang Yue, Zhiqi Chen, Rui Lu, et al.*2025 · 196 citations*](https://md.research.elamur.ai/free/en/papers/yue-2025-reinforcement-learning-really-incentivize-reasoning/)
286. [**What matters when building vision-language models?**Hugo Laurençon, Leo Tronchon, Matthieu Cord, et al.*2024 · 195 citations*](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2024-matters-building-vision-language-models/)
287. [**InstructUIE: Multi-task Instruction Tuning for Unified Information Extraction**Xiao Wang, Weikang Zhou, Can Zu, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-instructuie-multi-task-instruction-tuning/)
288. [**Okapi: Instruction-tuned Large Language Models in Multiple Languages with Reinforcement Learning from Human Feedback**Viet Lai, Chien Nguyen, Nghia Ngo, et al.*2023 · 194 citations*](https://md.research.elamur.ai/free/en/papers/lai-2023-okapi-instruction-tuned-large-language/)
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