---
title: "Who cites William Fedus — page 2"
description: "4,320 papers of the Elamur Research library cite the works of William Fedus, found in their bibliographies · page 2 of 4 · 101–200; the most cited first."
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---
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# Who cites William Fedus

4,883 citations of their works from 4,320 papers of our library (of the 140,884 whose bibliographies we read) · 352 with a page here, the most cited first · page 2 of 4 · 101–200 · [← William Fedus](https://md.research.elamur.ai/free/en/authors/william-fedus/)

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101. [**LegalBench: A Collaboratively Built Benchmark for Measuring Legal Reasoning in Large Language Models**Neel Guha; Julian Nyarko; Daniel E. Ho; Christopher Re; Adam Chilton; Aditya Narayana; Alex Chohlas-Wood; Austin Peters; Brandon Waldon; Daniel N. Rockmore; Diego Zambrano; Dmitry Talisman; Enam Hoque; Faiz Surani; Frank Fagan; Galit Sarfaty; Gregory M. Dickinson; Haggai Porat; Jason Hegland; Jessica Wu; Joe Nudell; Joel Niklaus; John Nay; Jonathan H. Choi; Kevin Tobia; Margaret Hagan; Megan Ma; Michael Livermore; Nikon Rasumov-Rahe; Nils Holzenberger; Noam Kolt; Peter Henderson; Sean Rehaag; Sharad Goel; Shang Gao; Spencer Williams; Sunny Gandhi; Tom Zur; Varun Iyer;, Zehua Li*2023 · 379 citations*](https://md.research.elamur.ai/free/en/papers/guha-2023-legalbench-collaboratively-built-benchmark-measuring/)
102. [**Mind2Web: Towards a Generalist Agent for the Web**Xiang Deng, Yu Gu, Boyuan Zheng, et al.*2023 · 376 citations*](https://md.research.elamur.ai/free/en/papers/deng-2023-mind2web-towards-generalist-agent-web/)
103. [**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/)
104. [**ChatDev: Communicative Agents for Software Development**Chen Qian, Wei Liu, Hongzhang Liu, et al.*2023 · 373 citations*](https://md.research.elamur.ai/free/en/papers/qian-2023-chatdev-communicative-agents-software/)
105. [**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/)
106. [**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/)
107. [**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/)
108. [**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/)
109. [**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/)
110. [**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/)
111. [**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/)
112. [**VideoChat: Chat-Centric Video Understanding**KunChang Li, Yinan He, Yi Wang, et al.*2023 · 351 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-videochat-chat-centric-video-understanding/)
113. [**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/)
114. [**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/)
115. [**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/)
116. [**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/)
117. [**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/)
118. [**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/)
119. [**Kimi K2: Open Agentic Intelligence**Kimi Team, Yifan Bai, Yiping Bao, et al.*2025 · 331 citations*](https://md.research.elamur.ai/free/en/papers/kimi-team-2025-kimi-k2-open-agentic/)
120. [**MM-Vet: Evaluating Large Multimodal Models for Integrated Capabilities**Weihao Yu, Zhengyuan Yang, Linjie Li, et al.*2023 · 328 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-mm-vet-evaluating-large-multimodal/)
121. [**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/)
122. [**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/)
123. [**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/)
124. [**Faith and Fate: Limits of Transformers on Compositionality**Nouha Dziri, Ximing Lu, Melanie Sclar, et al.*2023 · 313 citations*](https://md.research.elamur.ai/free/en/papers/dziri-2023-faith-fate-limits-transformers-compositionality/)
125. [**WANLI: Worker and AI Collaboration for Natural Language Inference Dataset Creation**Alisa Liu, Swabha Swayamdipta, Noah A. Smith, et al.*2022 · 312 citations*](https://md.research.elamur.ai/free/en/papers/liu-2022-wanli-worker-ai-collaboration-natural/)
126. [**Chameleon: Plug-and-Play Compositional Reasoning with Large Language Models**Pan Lu, Baolin Peng, Hao Cheng, et al.*2023 · 311 citations*](https://md.research.elamur.ai/free/en/papers/lu-2023-chameleon-plug-play-compositional-reasoning/)
127. [**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/)
128. [**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/)
129. [**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/)
130. [**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/)
131. [**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/)
132. [**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/)
133. [**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/)
134. [**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/)
135. [**Multi-step Jailbreaking Privacy Attacks on ChatGPT**Haoran Li, Dadi Guo, Wei Fan, et al.*2023 · 298 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-multi-step-jailbreaking-privacy-attacks/)
136. [**Large Language Models are Effective Text Rankers with Pairwise Ranking Prompting**Zhen Qin, Rolf Jagerman, Kai Hui, et al.*2024 · 296 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-large-language-models-are-effective/)
137. [**Multimodal Chain-of-Thought Reasoning in Language Models**Zhuosheng Zhang, Aston Zhang, Mu Li, et al.*2023 · 296 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-multimodal-chain-thought-reasoning-language/)
138. [**Prompting Large Language Model for Machine Translation: A Case Study**Biao Zhang, Barry Haddow, Alexandra Birch*2023 · 293 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-prompting-large-language-model-machine/)
139. [**MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning**Xiang Yue, Xingwei Qu, Ge Zhang, et al.*2023 · 288 citations*](https://md.research.elamur.ai/free/en/papers/yue-2023-mammoth-building-math-generalist-models/)
140. [**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/)
141. [**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/)
142. [**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/)
143. [**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/)
144. [**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/)
145. [**Hallucination-Free? Assessing the Reliability of Leading AI Legal Research Tools**Varun Magesh, Faiz Surani, Matthew Dahl, et al.*2024 · 267 citations*](https://md.research.elamur.ai/free/en/papers/magesh-2024-hallucination-free-assessing-reliability-leading/)
146. [**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/)
147. [**Towards Measuring the Representation of Subjective Global Opinions in Language Models**Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.*2023 · 262 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
148. [**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/)
149. [**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/)
150. [**Scaling Relationship on Learning Mathematical Reasoning with Large Language Models**Zheng Yuan, Hongyi Yuan, Chengpeng Li, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/yuan-2023-scaling-relationship-learning-mathematical-reasoning/)
151. [**Why Can GPT Learn In-Context? Language Models Secretly Perform Gradient Descent as Meta-Optimizers**Damai Dai, Yutao Sun, Li Dong, et al.*2023 · 252 citations*](https://md.research.elamur.ai/free/en/papers/dai-2023-why-can-gpt-learn-context/)
152. [**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/)
153. [**SEED-Bench: Benchmarking Multimodal LLMs with Generative Comprehension**Bohao Li, Rui Wang, Guangzhi Wang, et al.*2023 · 247 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-seed-bench-benchmarking-multimodal-llms/)
154. [**A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Models**S.M Towhidul Islam Tonmoy, S M Mehedi Zaman, Vinija Jain, et al.*2024 · 246 citations*](https://md.research.elamur.ai/free/en/papers/tonmoy-2024-comprehensive-survey-hallucination-mitigation-techniques/)
155. [**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/)
156. [**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/)
157. [**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/)
158. [**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/)
159. [**Trusting Your Evidence: Hallucinate Less with Context-aware Decoding**Weijia Shi, Xiaochuang Han, Mike Lewis, et al.*2024 · 240 citations*](https://md.research.elamur.ai/free/en/papers/shi-2024-trusting-your-evidence-hallucinate-less/)
160. [**LaMP: When Large Language Models Meet Personalization**Alireza Salemi, Sheshera Mysore, Michael Bendersky, et al.*2024 · 239 citations*](https://md.research.elamur.ai/free/en/papers/salemi-2024-lamp-when-large-language-models/)
161. [**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/)
162. [**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/)
163. [**On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey**Lin Long, Rui Wang, Ruixuan Xiao, et al.*2024 · 236 citations*](https://md.research.elamur.ai/free/en/papers/long-2024-llms-driven-synthetic-data-generation/)
164. [**Humanity's Last Exam**Long Phan, Alice Gatti, Ziwen Han, et al.*2025 · 235 citations*](https://md.research.elamur.ai/free/en/papers/phan-2025-humanity-s-last-exam/)
165. [**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/)
166. [**ChatGPT-4 Outperforms Experts and Crowd Workers in Annotating Political Twitter Messages with Zero-Shot Learning**Petter Tornberg*2023 · 230 citations*](https://md.research.elamur.ai/free/en/papers/tornberg-2023-chatgpt-4-outperforms-experts-crowd/)
167. [**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/)
168. [**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/)
169. [**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/)
170. [**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/)
171. [**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/)
172. [**FOLIO: Natural Language Reasoning with First-Order Logic**Simeng Han, Hailey Schoelkopf, Yilun Zhao, et al.*2022 · 217 citations*](https://md.research.elamur.ai/free/en/papers/han-2022-folio-natural-language-reasoning-first/)
173. [**Distilling Reasoning Capabilities into Smaller Language Models**Kumar Shridhar, Alessandro Stolfo, Mrinmaya Sachan*2023 · 216 citations*](https://md.research.elamur.ai/free/en/papers/shridhar-2023-distilling-reasoning-capabilities-into-smaller/)
174. [**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/)
175. [**Same Task, More Tokens: the Impact of Input Length on the Reasoning Performance of Large Language Models**Mosh Levy, Alon Jacoby, Yoav Goldberg*2024 · 212 citations*](https://md.research.elamur.ai/free/en/papers/levy-2024-same-task-more-tokens-impact/)
176. [**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/)
177. [**AgentVerse: Facilitating Multi-Agent Collaboration and Exploring Emergent Behaviors**Weize Chen, Yusheng Su, Jingwei Zuo, et al.*2023 · 210 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-agentverse-facilitating-multi-agent/)
178. [**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/)
179. [**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/)
180. [**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/)
181. [**Progress measures for grokking via mechanistic interpretability**Neel Nanda, Lawrence Chan, Tom Lieberum, et al.*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/nanda-2023-progress-measures-grokking-mechanistic-interpretability/)
182. [**Ring Attention with Blockwise Transformers for Near-Infinite Context**Hao Liu, Matei Zaharia, Pieter Abbeel*2023 · 207 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-ring-attention-blockwise-transformers-near/)
183. [**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/)
184. [**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/)
185. [**Prompting GPT-3 To Be Reliable**Chenglei Si, Zhe Gan, Zhengyuan Yang, et al.*2022 · 206 citations*](https://md.research.elamur.ai/free/en/papers/si-2022-prompting-gpt-3-reliable/)
186. [**Aya Dataset: An Open-Access Collection for Multilingual Instruction Tuning**Shivalika Singh, Freddie Vargus, Daniel D'souza, et al.*2024 · 202 citations*](https://md.research.elamur.ai/free/en/papers/singh-2024-aya-dataset-open-access-collection/)
187. [**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/)
188. [**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/)
189. [**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/)
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