---
title: "Who cites William Fedus — page 3"
description: "4,320 papers of the Elamur Research library cite the works of William Fedus, found in their bibliographies · page 3 of 4 · 201–300; 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 3 of 4 · 201–300 · [← William Fedus](https://md.research.elamur.ai/free/en/authors/william-fedus/)

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201. [**On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning**Omar Shaikh, Hongxin Zhang, William Held, et al.*2023 · 180 citations*](https://md.research.elamur.ai/free/en/papers/shaikh-2023-second-thought-let-s-not/)
202. [**CulturaX: A Cleaned, Enormous, and Multilingual Dataset for Large Language Models in 167 Languages**Thuat Nguyen, Chien Van Nguyen, Viet Dac Lai, et al.*2023 · 179 citations*](https://md.research.elamur.ai/free/en/papers/nguyen-2023-culturax-cleaned-enormous-multilingual-dataset/)
203. [**The False Promise of Imitating Proprietary LLMs**Arnav Gudibande, Eric Wallace, Charlie Snell, et al.*2023 · 178 citations*](https://md.research.elamur.ai/free/en/papers/gudibande-2023-false-promise-imitating-proprietary-llms/)
204. [**Evaluating Large Language Models: A Comprehensive Survey**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
205. [**ToolQA: A Dataset for LLM Question Answering with External Tools**Yuchen Zhuang, Yue Yu, Kuan Wang, et al.*2023 · 177 citations*](https://md.research.elamur.ai/free/en/papers/zhuang-2023-toolqa-dataset-llm-question-answering/)
206. [**LILA: A Unified Benchmark for Mathematical Reasoning**Swaroop Mishra, Matthew Finlayson, Pan Lu, et al.*2022 · 176 citations*](https://md.research.elamur.ai/free/en/papers/mishra-2022-lila-unified-benchmark-mathematical-reasoning/)
207. [**LINC: A Neurosymbolic Approach for Logical Reasoning by Combining Language Models with First-Order Logic Provers**Theo X. Olausson, Alex Gu, Benjamin Lipkin, et al.*2023 · 171 citations*](https://md.research.elamur.ai/free/en/papers/olausson-2023-linc-neurosymbolic-approach-logical-reasoning/)
208. [**Learning to Compress Prompts with Gist Tokens**Jesse Mu, Xiang Lisa Li, Noah Goodman*2023 · 170 citations*](https://md.research.elamur.ai/free/en/papers/mu-2023-learning-compress-prompts-gist-tokens/)
209. [**Survey on Factuality in Large Language Models: Knowledge, Retrieval and Domain-Specificity**Cunxiang Wang, Xiaoze Liu, Yuanhao Yue, et al.*2023 · 169 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-survey-factuality-large-language-models/)
210. [**Aligning Large Multimodal Models with Factually Augmented RLHF**Zhiqing Sun, Sheng Shen, Shengcao Cao, et al.*2023 · 168 citations*](https://md.research.elamur.ai/free/en/papers/sun-2023-aligning-large-multimodal-models-factually/)
211. [**Promptagator: Few-shot Dense Retrieval From 8 Examples**Zhuyun Dai, Vincent Y. Zhao, Ji Ma, et al.*2022 · 167 citations*](https://md.research.elamur.ai/free/en/papers/dai-2022-promptagator-few-shot-dense-retrieval/)
212. [**Generative Representational Instruction Tuning**Niklas Muennighoff, Hongjin Su, Liang Wang, et al.*2024 · 165 citations*](https://md.research.elamur.ai/free/en/papers/muennighoff-2024-generative-representational-instruction-tuning/)
213. [**Binding Language Models in Symbolic Languages**Zhoujun Cheng, Tianbao Xie, Peng Shi, et al.*2022 · 165 citations*](https://md.research.elamur.ai/free/en/papers/cheng-2022-binding-language-models-symbolic-languages/)
214. [**Do All Languages Cost the Same? Tokenization in the Era of Commercial Language Models**Orevaoghene Ahia, Sachin Kumar, Hila Gonen, et al.*2023 · 164 citations*](https://md.research.elamur.ai/free/en/papers/ahia-2023-do-all-languages-cost-same/)
215. [**Improve Mathematical Reasoning in Language Models by Automated Process Supervision**Liangchen Luo, Yinxiao Liu, Rosanne Liu, et al.*2024 · 163 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-improve-mathematical-reasoning-language-models/)
216. [**Weak-to-Strong Generalization: Eliciting Strong Capabilities With Weak Supervision**Collin Burns, Pavel Izmailov, Jan Hendrik Kirchner, et al.*2023 · 163 citations*](https://md.research.elamur.ai/free/en/papers/burns-2023-weak-strong-generalization-eliciting-strong/)
217. [**Catastrophic Jailbreak of Open-source LLMs via Exploiting Generation**Yangsibo Huang, Samyak Gupta, Mengzhou Xia, et al.*2023 · 162 citations*](https://md.research.elamur.ai/free/en/papers/huang-2023-catastrophic-jailbreak-open-source-llms/)
218. [**Orca 2: Teaching Small Language Models How to Reason**Arindam Mitra, Luciano Del Corro, Shweti Mahajan, et al.*2023 · 160 citations*](https://md.research.elamur.ai/free/en/papers/mitra-2023-orca-2-teaching-small-language/)
219. [**Reasoning with Language Model Prompting: A Survey**Shuofei Qiao, Yixin Ou, Ningyu Zhang, et al.*2022 · 160 citations*](https://md.research.elamur.ai/free/en/papers/qiao-2022-reasoning-language-model-prompting-survey/)
220. [**LLMLingua-2: Data Distillation for Efficient and Faithful Task-Agnostic Prompt Compression**Zhuoshi Pan, Qianhui Wu, Huiqiang Jiang, et al.*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/pan-2024-llmlingua-2-data-distillation-efficient/)
221. [**MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documents**Liyan Tang, Philippe Laban, Greg Durrett*2024 · 159 citations*](https://md.research.elamur.ai/free/en/papers/tang-2024-minicheck-efficient-fact-checking-llms/)
222. [**GLM-4.5: Agentic, Reasoning, and Coding (ARC) Foundation Models**Aohan Zeng, Xin Lv, Qinkai Zheng, et al.*2025 · 157 citations*](https://md.research.elamur.ai/free/en/papers/team-2025-4-glm-4-5-agentic/)
223. [**DataComp-LM: In search of the next generation of training sets for language models**Jeffrey Li, Alex Fang, Georgios Smyrnis, et al.*2024 · 156 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-datacomp-lm-search-next-generation/)
224. [**VisualWebArena: Evaluating Multimodal Agents on Realistic Visual Web Tasks**Jing Yu Koh, Robert Lo, Lawrence Jang, et al.*2024 · 156 citations*](https://md.research.elamur.ai/free/en/papers/koh-2024-visualwebarena-evaluating-multimodal-agents-realistic/)
225. [**ReConcile: Round-Table Conference Improves Reasoning via Consensus among Diverse LLMs**Justin Chen, Swarnadeep Saha, Mohit Bansal*2024 · 154 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-reconcile-round-table-conference-improves/)
226. [**TheoremQA: A Theorem-driven Question Answering Dataset**Wenhu Chen, Ming Yin, Max Ku, et al.*2023 · 154 citations*](https://md.research.elamur.ai/free/en/papers/chen-2023-theoremqa-theorem-driven-question-answering/)
227. [**Leveraging Large Language Models for Multiple Choice Question Answering**Joshua Robinson, Christopher Michael Rytting, David Wingate*2022 · 154 citations*](https://md.research.elamur.ai/free/en/papers/robinson-2022-leveraging-large-language-models-multiple/)
228. [**SFT Memorizes, RL Generalizes: A Comparative Study of Foundation Model Post-training**Tianzhe Chu, Yuexiang Zhai, Jihan Yang, et al.*2025 · 151 citations*](https://md.research.elamur.ai/free/en/papers/chu-2025-sft-memorizes-rl-generalizes-comparative/)
229. [**The Dawn After the Dark: An Empirical Study on Factuality Hallucination in Large Language Models**Junyi Li, Jie Chen, Ruiyang Ren, et al.*2024 · 150 citations*](https://md.research.elamur.ai/free/en/papers/li-2024-dawn-after-dark-empirical-study/)
230. [**Language Model Tokenizers Introduce Unfairness Between Languages**Aleksandar Petrov, Emanuele La Malfa, Philip H. S. Torr, et al.*2023 · 149 citations*](https://md.research.elamur.ai/free/en/papers/petrov-2023-language-model-tokenizers-introduce-unfairness/)
231. [**Phi-4-Mini Technical Report: Compact yet Powerful Multimodal Language Models via Mixture-of-LoRAs**Microsoft, Abdelrahman Abouelenin, Atabak Ashfaq, et al.*2025 · 145 citations*](https://md.research.elamur.ai/free/en/papers/microsoft-2025-phi-4-mini-technical-report/)
232. [**How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition**Guanting Dong, Hongyi Yuan, Keming Lu, et al.*2024 · 145 citations*](https://md.research.elamur.ai/free/en/papers/dong-2024-how-abilities-large-language-models/)
233. [**Context-faithful Prompting for Large Language Models**Wenxuan Zhou, Sheng Zhang, Hoifung Poon, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/zhou-2023-context-faithful-prompting-large-language/)
234. [**Fact-Checking Complex Claims with Program-Guided Reasoning**Liangming Pan, Xiaobao Wu, Xinyuan Lu, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/pan-2023-fact-checking-complex-claims-program/)
235. [**RA-DIT: Retrieval-Augmented Dual Instruction Tuning**Xi Victoria Lin, Xilun Chen, Mingda Chen, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/lin-2023-ra-dit-retrieval-augmented-dual/)
236. [**SCOTT: Self-Consistent Chain-of-Thought Distillation**Peifeng Wang, Zhengyang Wang, Zheng Li, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-scott-self-consistent-chain-thought/)
237. [**Self-Evaluation Guided Beam Search for Reasoning**Yuxi Xie, Kenji Kawaguchi, Yiran Zhao, et al.*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/xie-2023-self-evaluation-guided-beam-search/)
238. [**Executable Code Actions Elicit Better LLM Agents**Xingyao Wang, Yangyi Chen, Lifan Yuan, et al.*2024 · 143 citations*](https://md.research.elamur.ai/free/en/papers/wang-2024-executable-code-actions-elicit-better/)
239. [**Generative Verifiers: Reward Modeling as Next-Token Prediction**Lunjun Zhang, Arian Hosseini, Hritik Bansal, et al.*2024 · 143 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2024-generative-verifiers-reward-modeling-as/)
240. [**MiniLLM: On-Policy Distillation of Large Language Models**Yuxian Gu, Li Dong, Furu Wei, et al.*2023 · 143 citations*](https://md.research.elamur.ai/free/en/papers/gu-2023-minillm-policy-distillation-large-language/)
241. [**PMC-LLaMA: Towards Building Open-source Language Models for Medicine**Chaoyi Wu, Weixiong Lin, Xiaoman Zhang, et al.*2023 · 142 citations*](https://md.research.elamur.ai/free/en/papers/wu-2023-pmc-llama-towards-building-open/)
242. [**JudgeLM: Fine-tuned Large Language Models are Scalable Judges**Lianghui Zhu, Xinggang Wang, Xinlong Wang*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-judgelm-fine-tuned-large-language/)
243. [**How does GPT-2 compute greater-than?: Interpreting mathematical abilities in a pre-trained language model**Michael Hanna, Ollie Liu, Alexandre Variengien*2023 · 140 citations*](https://md.research.elamur.ai/free/en/papers/hanna-2023-gpt-2-compute-greater-than/)
244. [**Unleashing the Emergent Cognitive Synergy in Large Language Models: A Task-Solving Agent through Multi-Persona Self-Collaboration**Zhenhailong Wang, Shaoguang Mao, Wenshan Wu, et al.*2023 · 139 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-unleashing-emergent-cognitive-synergy-large/)
245. [**DeepInception: Hypnotize Large Language Model to Be Jailbreaker**Xuan Li, Zhanke Zhou, Jianing Zhu, et al.*2023 · 138 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-deepinception-hypnotize-large-language-model/)
246. [**Investigating Data Contamination in Modern Benchmarks for Large Language Models**Chunyuan Deng, Yilun Zhao, Xiangru Tang, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/deng-2024-investigating-data-contamination-modern-benchmarks/)
247. [**A fine-grained comparison of pragmatic language understanding in humans and language models**Jennifer Hu, Sammy Floyd, Olessia Jouravlev, et al.*2023 · 136 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-fine-grained-comparison-pragmatic-language/)
248. [**Attributed Question Answering: Evaluation and Modeling for Attributed Large Language Models**Bernd Bohnet, Vinh Q. Tran, Pat Verga, et al.*2022 · 136 citations*](https://md.research.elamur.ai/free/en/papers/bohnet-2022-attributed-question-answering-evaluation-modeling/)
249. [**A Roadmap to Pluralistic Alignment**Taylor Sorensen, Jared Moore, Jillian Fisher, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/sorensen-2024-roadmap-pluralistic-alignment/)
250. [**Error Analysis Prompting Enables Human-Like Translation Evaluation in Large Language Models**Qingyu Lu, Baopu Qiu, Liang Ding, et al.*2024 · 135 citations*](https://md.research.elamur.ai/free/en/papers/lu-2024-error-analysis-prompting-enables-human/)
251. [**InFoBench: Evaluating Instruction Following Ability in Large Language Models**Yiwei Qin, Kaiqiang Song, Yebowen Hu, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/qin-2024-infobench-evaluating-instruction-following-ability/)
252. [**To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning**Zayne Sprague, Fangcong Yin, Juan Diego Rodriguez, et al.*2024 · 133 citations*](https://md.research.elamur.ai/free/en/papers/sprague-2024-cot-cot-chain-thought-helps/)
253. [**Prompting is not a substitute for probability measurements in large language models**Jennifer Hu, Roger Levy*2023 · 133 citations*](https://md.research.elamur.ai/free/en/papers/hu-2023-prompting-is-not-substitute-probability/)
254. [**Self-Alignment with Instruction Backtranslation**Xian Li, Ping Yu, Chunting Zhou, et al.*2023 · 131 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-self-alignment-instruction-backtranslation/)
255. [**Task-aware Retrieval with Instructions**Akari Asai, Timo Schick, Patrick Lewis, et al.*2023 · 131 citations*](https://md.research.elamur.ai/free/en/papers/asai-2023-task-aware-retrieval-instructions/)
256. [**Understanding the Effects of RLHF on LLM Generalisation and Diversity**Robert Kirk, Ishita Mediratta, Christoforos Nalmpantis, et al.*2023 · 131 citations*](https://md.research.elamur.ai/free/en/papers/kirk-2023-understanding-effects-rlhf-llm-generalisation/)
257. [**FLUTE: Figurative Language Understanding through Textual Explanations**Tuhin Chakrabarty, Arkadiy Saakyan, Debanjan Ghosh, et al.*2022 · 131 citations*](https://md.research.elamur.ai/free/en/papers/chakrabarty-2022-flute-figurative-language-understanding-through/)
258. [**GPT-4V(ision) is a Generalist Web Agent, if Grounded**Boyuan Zheng, Boyu Gou, Jihyung Kil, et al.*2024 · 130 citations*](https://md.research.elamur.ai/free/en/papers/zheng-2024-gpt-4v-ision-generalist-web/)
259. [**FreshLLMs: Refreshing Large Language Models with Search Engine Augmentation**Tu Vu, Mohit Iyyer, Xuezhi Wang, et al.*2023 · 130 citations*](https://md.research.elamur.ai/free/en/papers/vu-2023-freshllms-refreshing-large-language-models/)
260. [**MathDial: A Dialogue Tutoring Dataset with Rich Pedagogical Properties Grounded in Math Reasoning Problems**Jakub Macina, Nico Daheim, Sankalan Chowdhury, et al.*2023 · 130 citations*](https://md.research.elamur.ai/free/en/papers/macina-2023-mathdial-dialogue-tutoring-dataset-rich/)
261. [**Faithful Logical Reasoning via Symbolic Chain-of-Thought**Jundong Xu, Hao Fei, Liangming Pan, et al.*2024 · 129 citations*](https://md.research.elamur.ai/free/en/papers/xu-2024-faithful-logical-reasoning-symbolic-chain/)
262. [**Improving Language Model Negotiation with Self-Play and In-Context Learning from AI Feedback**Yao Fu, Hao Peng, Tushar Khot, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/fu-2023-improving-language-model-negotiation-self/)
263. [**Retrieval meets Long Context Large Language Models**Peng Xu, Wei Ping, Xianchao Wu, et al.*2023 · 129 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-retrieval-meets-long-context-large/)
264. [**Deductive Verification of Chain-of-Thought Reasoning**Zhan Ling, Yunhao Fang, Xuanlin Li, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/ling-2023-deductive-verification-chain-thought-reasoning/)
265. [**ZeroSCROLLS: A Zero-Shot Benchmark for Long Text Understanding**Uri Shaham, Maor Ivgi, Avia Efrat, et al.*2023 · 128 citations*](https://md.research.elamur.ai/free/en/papers/shaham-2023-zeroscrolls-zero-shot-benchmark-long/)
266. [**Time Travel in LLMs: Tracing Data Contamination in Large Language Models**Shahriar Golchin, Mihai Surdeanu*2023 · 127 citations*](https://md.research.elamur.ai/free/en/papers/golchin-2023-time-travel-llms-tracing-data/)
267. [**Safety-Tuned LLaMAs: Lessons From Improving the Safety of Large Language Models that Follow Instructions**Federico Bianchi, Mirac Suzgun, Giuseppe Attanasio, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/bianchi-2023-safety-tuned-llamas-lessons-improving/)
268. [**Large Language Model Alignment: A Survey**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
269. [**Fine-tuning Language Models for Factuality**Katherine Tian, Eric Mitchell, Huaxiu Yao, et al.*2023 · 120 citations*](https://md.research.elamur.ai/free/en/papers/tian-2023-fine-tuning-language-models-factuality/)
270. [**DeepSeek-V4: Towards Highly Efficient Million-Token Context Intelligence**DeepSeek-AI, Anyi Xu, Bangcai Lin, et al.*2026 · 117 citations*](https://md.research.elamur.ai/free/en/papers/deepseek-ai-2026-deepseek-v4-highly-efficient/)
271. [**From Persona to Personalization: A Survey on Role-Playing Language Agents**Jiangjie Chen, Xintao Wang, Rui Xu, et al.*2024 · 114 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-persona-personalization-survey-role-playing/)
272. [**GoLLIE: Annotation Guidelines improve Zero-Shot Information-Extraction**Oscar Sainz, Iker Garcia-Ferrero, Rodrigo Agerri, et al.*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/sainz-2023-gollie-annotation-guidelines-improve-zero/)
273. [**RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentation**Fangyuan Xu, Weijia Shi, Eunsol Choi*2023 · 114 citations*](https://md.research.elamur.ai/free/en/papers/xu-2023-recomp-improving-retrieval-augmented-lms/)
274. [**ALLaM: Large Language Models for Arabic and English**M Saiful Bari, Yazeed Alnumay, Norah A. Alzahrani, et al.*2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/bari-2024-allam-large-language-models-arabic/)
275. [**Quiet-STaR: Language models can teach themselves to think before speaking***2024 · 113 citations*](https://md.research.elamur.ai/free/en/papers/al-2024-quiet-star-language-models-teach/)
276. [**Olmo 3**Team Olmo: Allyson Ettinger, Amanda Bertsch, Bailey Kuehl, et al.*2025 · 109 citations*](https://md.research.elamur.ai/free/en/papers/ettinger-2025-olmo-3/)
277. [**ExpertQA: Expert-Curated Questions and Attributed Answers**Chaitanya Malaviya, Subin Lee, Sihao Chen, et al.*2024 · 109 citations*](https://md.research.elamur.ai/free/en/papers/malaviya-2024-expertqa-expert-curated-questions-attributed/)
278. [**RestGPT: Connecting Large Language Models with Real-World RESTful APIs**Yifan Song, Weimin Xiong, Dawei Zhu, et al.*2023 · 108 citations*](https://md.research.elamur.ai/free/en/papers/song-2023-restgpt-connecting-large-language-models/)
279. [**Evaluating the Performance of Large Language Models on GAOKAO Benchmark**Xiaotian Zhang, Chunyang Li, Yi Zong, et al.*2023 · 107 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-evaluating-performance-large-language/)
280. [**ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models**Jinheon Baek, Sujay Kumar Jauhar, Silviu Cucerzan, et al.*2024 · 106 citations*](https://md.research.elamur.ai/free/en/papers/baek-2024-researchagent-iterative-research-idea/)
281. [**LENS: A Learnable Evaluation Metric for Text Simplification**Mounica Maddela, Yao Dou, David Heineman, et al.*2023 · 103 citations*](https://md.research.elamur.ai/free/en/papers/maddela-2023-lens-learnable-evaluation-metric-text/)
282. [**Inverse Scaling: When Bigger Isn't Better**Ian R. McKenzie, Alexander Lyzhov, Michael Pieler, et al.*2023 · 99 citations*](https://md.research.elamur.ai/free/en/papers/mckenzie-2023-inverse-scaling-bigger-isn-t/)
283. [**MathCoder: Seamless Code Integration in LLMs for Enhanced Mathematical Reasoning**Ke Wang, Houxing Ren, Aojun Zhou, et al.*2023 · 98 citations*](https://md.research.elamur.ai/free/en/papers/wang-2023-mathcoder-seamless-code-integration-llms/)
284. [**LongAlign: A Recipe for Long Context Alignment of Large Language Models**Yushi Bai, Xin Lv, Jiajie Zhang, et al.*2024 · 97 citations*](https://md.research.elamur.ai/free/en/papers/bai-2024-longalign-recipe-long-context/)
285. [**MAmmoTH2: Scaling Instructions from the Web**Xiang Yue, Tuney Zheng, Ge Zhang, et al.*2024 · 94 citations*](https://md.research.elamur.ai/free/en/papers/yue-2024-mammoth2-scaling-instructions-web/)
286. [**GLM-5: from Vibe Coding to Agentic Engineering**Aohan Zeng, Xin Lv, Zhenyu Hou, et al.*2026 · 89 citations*](https://md.research.elamur.ai/free/en/papers/team-2026-5-glm-5-vibe-coding/)
287. [**The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs**Laura Ruis, Akbir Khan, Stella Biderman, et al.*2022 · 86 citations*](https://md.research.elamur.ai/free/en/papers/ruis-2022-goldilocks-pragmatic-understanding-fine-tuning/)
288. [**NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation**Kaustubh Dhole, Varun Gangal, Sebastian Gehrmann, et al.*2023 · 84 citations*](https://md.research.elamur.ai/free/en/papers/dhole-2023-nl-augmenter-framework-task-sensitive/)
289. [**PUB: A Pragmatics Understanding Benchmark for Assessing LLMs' Pragmatics Capabilities**Settaluri Sravanthi, Meet Doshi, Pavan Tankala, et al.*2024 · 75 citations*](https://md.research.elamur.ai/free/en/papers/sravanthi-2024-pub-pragmatics-understanding-benchmark-assessing/)
290. [**Beyond Chinchilla-Optimal: Accounting for Inference in Language Model Scaling Laws**Nikhil Sardana, Jacob Portes, Sasha Doubov, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/sardana-2024-chinchilla-optimal-accounting-inference-language/)
291. [**Metaphor Understanding Challenge Dataset for LLMs**Xiaoyu Tong, Rochelle Choenni, Martha Lewis, et al.*2024 · 47 citations*](https://md.research.elamur.ai/free/en/papers/tong-2024-metaphor-understanding-challenge-dataset-llms/)
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