---
title: "Who cites Adrian Weller — 1,023 papers of our library"
description: "1,023 papers of the Elamur Research library cite the works of Adrian Weller, found in their bibliographies · page 1 of 4 · 1–100; the most cited first."
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---
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# Who cites Adrian Weller

1,031 citations of their works from 1,023 papers of our library (of the 149,439 whose bibliographies we read) · 321 with a page here, the most cited first · page 1 of 4 · 1–100 · [← Adrian Weller](https://md.research.elamur.ai/free/en/authors/adrian-weller/)

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1. **[Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://md.research.elamur.ai/free/en/papers/liu-2021-swin-transformer-hierarchical-vision-transformer/) [IEEE/CVF International Conference on Computer Vision (ICCV)](https://md.research.elamur.ai/free/en/venues/ieee-cvf-international-conference-on-computer-vision-iccv/)**Ze Liu, Yutong Lin, Yue Cao, et al.*2021 · 33,968 citations*
2. [**GPT-4 Technical Report arXiv:2303.08774**OpenAI, Josh Achiam, Steven Adler, et al.*2023 · 13,036 citations*](https://md.research.elamur.ai/free/en/papers/openai-2023-gpt-4-technical-report/)
3. [**The Llama 3 Herd of Models arXiv:2407.21783**Aaron Grattafiori, Abhimanyu Dubey, Abhinav Jauhri, et al.*2024 · 7,682 citations*](https://md.research.elamur.ai/free/en/papers/grattafiori-2024-llama-3-herd-models/)
4. [**PaLM: Scaling Language Modeling with Pathways arXiv:2204.02311**Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, et al.*2022 · 4,753 citations*](https://md.research.elamur.ai/free/en/papers/chowdhery-2022-palm-scaling-language-modeling-pathways/)
5. **[A Survey on Evaluation of Large Language Models](https://md.research.elamur.ai/free/en/papers/chang-2023-survey-evaluation-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Yupeng Chang, Xu Wang, Jindong Wang, et al.*2023 · 3,648 citations*
6. [**On the Opportunities and Risks of Foundation Models arXiv:2108.07258**Rishi Bommasani, Drew A. Hudson, Ehsan Adeli, et al.*2021 · 3,160 citations*](https://md.research.elamur.ai/free/en/papers/bommasani-2021-opportunities-risks-foundation-models/)
7. [**RoFormer: Enhanced Transformer with Rotary Position Embedding arXiv:2104.09864**Jianlin Su, Yu Lu, Shengfeng Pan, et al.*2021 · 3,028 citations*](https://md.research.elamur.ai/free/en/papers/su-2021-roformer-enhanced-transformer-rotary-position/)
8. [**DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models arXiv:2402.03300**Zhihong Shao, Peiyi Wang, Qihao Zhu, et al.*2024 · 2,115 citations*](https://md.research.elamur.ai/free/en/papers/shao-2024-deepseekmath-pushing-limits-mathematical-reasoning/)
9. [**Qwen Technical Report arXiv:2309.16609**Jinze Bai, Shuai Bai, Yunfei Chu, et al.*2023 · 1,669 citations*](https://md.research.elamur.ai/free/en/papers/bai-2023-qwen-technical-report/)
10. **[Efficient Transformers: A Survey](https://md.research.elamur.ai/free/en/papers/tay-2020-efficient-transformers-survey/) [ACM Computing Surveys](https://md.research.elamur.ai/free/en/venues/acm-computing-surveys/)**Yi Tay, Mostafa Dehghani, Dara Bahri, et al.*2020 · 1,539 citations*
11. **[A Comprehensive Overview of Large Language Models](https://md.research.elamur.ai/free/en/papers/naveed-2023-comprehensive-overview-large-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Humza Naveed, Asad Ullah Khan, Shi Qiu, et al.*2023 · 1,287 citations*
12. [**FlashAttention-2: Faster Attention with Better Parallelism and Work Partitioning arXiv:2307.08691**Tri Dao*2023 · 1,240 citations*](https://md.research.elamur.ai/free/en/papers/dao-2023-flashattention-2-faster-attention-better/)
13. [**OpenAI o1 System Card arXiv:2412.16720**OpenAI, Aaron Jaech, Adam Kalai, et al.*2024 · 1,110 citations*](https://md.research.elamur.ai/free/en/papers/openai-2024-openai-o1-system-card/)
14. [**Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges arXiv:2103.11251 · Statistics Surveys, 2021**Cynthia Rudin; Chaofan Chen; Zhi Chen; Haiyang Huang; Lesia Semenova;, Chudi Zhong*2021 · 974 citations*](https://md.research.elamur.ai/free/en/papers/rudin-2021-interpretable-machine-learning-fundamental/)
15. **[ImageBind: One Embedding Space To Bind Them All](https://md.research.elamur.ai/free/en/papers/girdhar-2023-imagebind-one-embedding-space-bind/) [IEEE Computer Society Conference on Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/venues/ieee-computer-society-conference-on-computer-vision-and-pattern-recognition/)**Rohit Girdhar, Alaaeldin El-Nouby, Zhuang Liu, et al.*2023 · 954 citations*
16. **[s1: Simple test-time scaling](https://md.research.elamur.ai/free/en/papers/muennighoff-2025-s1-simple-test-time-scaling/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Niklas Muennighoff, Zitong Yang, Weijia Shi, et al.*2025 · 608 citations*
17. **[FNet: Mixing Tokens with Fourier Transforms](https://md.research.elamur.ai/free/en/papers/thorp-2022-fnet-mixing-tokens-fourier-transforms/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**James Lee-Thorp, Joshua Ainslie, Ilya Eckstein, et al.*2022 · 597 citations*
18. **[AudioLM: a Language Modeling Approach to Audio Generation](https://md.research.elamur.ai/free/en/papers/borsos-2022-audiolm-language-modeling-approach-audio/) [IEEE/ACM Transactions on Audio Speech and Language Processing](https://md.research.elamur.ai/free/en/venues/ieee-acm-transactions-on-audio-speech-and-language-processing/)**Zalan Borsos, Raphael Marinier, Damien Vincent, et al.*2022 · 581 citations*
19. [**MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models arXiv:2309.12284**Longhui Yu, Weisen Jiang, Han Shi, et al.*2023 · 565 citations*](https://md.research.elamur.ai/free/en/papers/yu-2023-metamath-bootstrap-your-own-mathematical/)
20. **[How Far Are We to GPT-4V? Closing the Gap to Commercial Multimodal Models with Open-Source Suites](https://md.research.elamur.ai/free/en/papers/chen-2024-far-gpt-4v-closing/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Zhe Chen, Weiyun Wang, Hao Tian, et al.*2024 · 541 citations*
21. [**Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model arXiv:2401.09417**Lianghui Zhu, Bencheng Liao, Qian Zhang, et al.*2024 · 500 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2024-vision-mamba-efficient-visual-representation/)
22. **[Math-Shepherd: Verify and Reinforce LLMs Step-by-step without Human Annotations](https://md.research.elamur.ai/free/en/papers/wang-2024-math-shepherd-verify-reinforce-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Peiyi Wang, Lei Li, Zhihong Shao, et al.*2024 · 497 citations*
23. [**Red Teaming Language Models to Reduce Harms: Methods, Scaling Behaviors, and Lessons Learned arXiv:2209.07858**Deep Ganguli, Liane Lovitt, Jackson Kernion, et al.*2022 · 485 citations*](https://md.research.elamur.ai/free/en/papers/ganguli-2022-red-teaming-language-models-reduce/)
24. **[OlympiadBench: A Challenging Benchmark for Promoting AGI with Olympiad-Level Bilingual Multimodal Scientific Problems](https://md.research.elamur.ai/free/en/papers/he-2024-olympiadbench-challenging-benchmark-promoting-agi/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Chaoqun He, Renjie Luo, Yuzhuo Bai, et al.*2024 · 456 citations*
25. [**Long Range Arena: A Benchmark for Efficient Transformers arXiv:2011.04006**Yi Tay, Mostafa Dehghani, Samira Abnar, et al.*2020 · 414 citations*](https://md.research.elamur.ai/free/en/papers/tay-2020-long-range-arena-benchmark-efficient/)
26. **[FlashAttention-3: Fast and Accurate Attention with Asynchrony and Low-precision](https://md.research.elamur.ai/free/en/papers/shah-2024-flashattention-3-fast-accurate-attention/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jay Shah, Ganesh Bikshandi, Ying Zhang, et al.*2024 · 400 citations*
27. **[LongT5: Efficient Text-To-Text Transformer for Long Sequences](https://md.research.elamur.ai/free/en/papers/guo-2022-longt5-efficient-text-text-transformer/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Mandy Guo, Joshua Ainslie, David Uthus, et al.*2022 · 399 citations*
28. [**WizardMath: Empowering Mathematical Reasoning for Large Language Models via Reinforced Evol-Instruct arXiv:2308.09583**Haipeng Luo, Qingfeng Sun, Can Xu, et al.*2023 · 372 citations*](https://md.research.elamur.ai/free/en/papers/luo-2023-wizardmath-empowering-mathematical-reasoning-large/)
29. **[Efficient Attentions for Long Document Summarization](https://md.research.elamur.ai/free/en/papers/huang-2021-efficient-attentions-long-document-summarization/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Luyang Huang, Shuyang Cao, Nikolaus Parulian, et al.*2021 · 336 citations*
30. [**Socratic Models: Composing Zero-Shot Multimodal Reasoning with Language arXiv:2204.00598**Andy Zeng, Maria Attarian, Brian Ichter, et al.*2022 · 302 citations*](https://md.research.elamur.ai/free/en/papers/zeng-2022-socratic-models-composing-zero-shot/)
31. [**Challenges and Applications of Large Language Models arXiv:2307.10169**Jean Kaddour, Joshua Harris, Maximilian Mozes, et al.*2023 · 301 citations*](https://md.research.elamur.ai/free/en/papers/kaddour-2023-challenges-applications-large-language-models/)
32. **[Canine: Pre-training an Efficient Tokenization-Free Encoder for Language Representation](https://md.research.elamur.ai/free/en/papers/clark-2022-canine-pre-training-efficient-tokenization/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Jonathan H. Clark, Dan Garrette, Iulia Turc, et al.*2022 · 301 citations*
33. **[xLSTM: Extended Long Short-Term Memory](https://md.research.elamur.ai/free/en/papers/beck-2024-xlstm-extended-long-short-term/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Maximilian Beck, Korbinian Pöppel, Markus Spanring, et al.*2024 · 296 citations*
34. [**MAmmoTH: Building Math Generalist Models through Hybrid Instruction Tuning arXiv:2309.05653**Xiang Yue, Xingwei Qu, Ge Zhang, et al.*2023 · 295 citations*](https://md.research.elamur.ai/free/en/papers/yue-2023-mammoth-building-math-generalist-models/)
35. [**Expanding Performance Boundaries of Open-Source Multimodal Models with Model, Data, and Test-Time Scaling arXiv:2412.05271**Zhe Chen, Weiyun Wang, Yue Cao, et al.*2024 · 287 citations*](https://md.research.elamur.ai/free/en/papers/chen-2024-expanding-performance-boundaries-open/)
36. [**Llemma: An Open Language Model For Mathematics arXiv:2310.10631**Zhangir Azerbayev, Hailey Schoelkopf, Keiran Paster, et al.*2023 · 286 citations*](https://md.research.elamur.ai/free/en/papers/azerbayev-2023-llemma-open-language-model-mathematics/)
37. **[Large Language Models for Mathematical Reasoning: Progresses and Challenges](https://md.research.elamur.ai/free/en/papers/ahn-2024-large-language-models-mathematical-reasoning/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Janice Ahn, Rishu Verma, Renze Lou, et al.*2024 · 280 citations*
38. [**Perceiver: General Perception with Iterative Attention arXiv:2103.03206**Andrew Jaegle, Felix Gimeno, Andrew Brock, et al.*2021 · 269 citations*](https://md.research.elamur.ai/free/en/papers/jaegle-2021-perceiver-general-perception-iterative-attention/)
39. **[Predictability and Surprise in Large Generative Models](https://md.research.elamur.ai/free/en/papers/ganguli-2022-predictability-surprise-large-generative-models/) [ACM Conference on Fairness, Accountability, and Transparency (FAccT)](https://md.research.elamur.ai/free/en/venues/acm-conference-on-fairness-accountability-and-transparency-facct/)**Deep Ganguli, Danny Hernandez, Liane Lovitt, et al.*2022 · 268 citations*
40. [**Transformers learn in-context by gradient descent arXiv:2212.07677**Johannes von Oswald, Eyvind Niklasson, Ettore Randazzo, et al.*2022 · 247 citations*](https://md.research.elamur.ai/free/en/papers/oswald-2022-transformers-learn-context-gradient-descent/)
41. **[On LLMs-Driven Synthetic Data Generation, Curation, and Evaluation: A Survey](https://md.research.elamur.ai/free/en/papers/long-2024-llms-driven-synthetic-data-generation/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Lin Long, Rui Wang, Ruixuan Xiao, et al.*2024 · 237 citations*
42. **[What matters when building vision-language models?](https://md.research.elamur.ai/free/en/papers/lauren-ccon-2024-matters-building-vision-language-models/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hugo Laurençon, Leo Tronchon, Matthieu Cord, et al.*2024 · 212 citations*
43. [**LIMO: Less is More for Reasoning arXiv:2502.03387**Yixin Ye, Zhen Huang, Yang Xiao, et al.*2025 · 209 citations*](https://md.research.elamur.ai/free/en/papers/ye-2025-limo-less-more-reasoning/)
44. [**AlphaEvolve: A coding agent for scientific and algorithmic discovery arXiv:2506.13131**Alexander Novikov, Ngân Vũ, Marvin Eisenberger, et al.*2025 · 202 citations*](https://md.research.elamur.ai/free/en/papers/novikov-2025-alphaevolve-coding-agent-scientific-algorithmic/)
45. **[ReST-MCTS*: LLM Self-Training via Process Reward Guided Tree Search](https://md.research.elamur.ai/free/en/papers/zhang-2024-rest-mcts-llm-self-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Dan Zhang, Sining Zhoubian, Ziniu Hu, et al.*2024 · 200 citations*
46. [**Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers arXiv:2409.04109**Chenglei Si, Diyi Yang, Tatsunori Hashimoto*2024 · 196 citations*](https://md.research.elamur.ai/free/en/papers/si-2024-llms-generate-novel-research/)
47. [**Charformer: Fast Character Transformers via Gradient-based Subword Tokenization arXiv:2106.12672**Yi Tay, Vinh Q. Tran, Sebastian Ruder, et al.*2021 · 186 citations*](https://md.research.elamur.ai/free/en/papers/tay-2021-charformer-fast-character-transformers-gradient/)
48. **[Adapting Language Models to Compress Contexts](https://md.research.elamur.ai/free/en/papers/chevalier-2023-adapting-language-models-compress-contexts/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Alexis Chevalier, Alexander Wettig, Anirudh Ajith, et al.*2023 · 182 citations*
49. [**Evaluating Large Language Models: A Comprehensive Survey arXiv:2310.19736**Zishan Guo, Renren Jin, Chuang Liu, et al.*2023 · 182 citations*](https://md.research.elamur.ai/free/en/papers/guo-2023-evaluating-large-language-models-comprehensive/)
50. [**SmolLM2: When Smol Goes Big — Data-Centric Training of a Small Language Model arXiv:2502.02737**Loubna Ben Allal, Anton Lozhkov, Elie Bakouch, et al.*2025 · 181 citations*](https://md.research.elamur.ai/free/en/papers/allal-2025-smollm2-smol-goes-big-data/)
51. **[Towards Reasoning Era: A Survey of Long Chain-of-Thought for Reasoning Large Language Models](https://md.research.elamur.ai/free/en/papers/chen-2025-towards-reasoning-era-survey-long/) [Science China Information Sciences](https://md.research.elamur.ai/free/en/venues/science-china-information-sciences/)**Qiguang Chen, Libo Qin, Jinhao Liu, et al.*2025 · 176 citations*
52. [**Scaling Synthetic Data Creation with 1,000,000,000 Personas arXiv:2406.20094**Tao Ge, Xin Chan, Xiaoyang Wang, et al.*2024 · 171 citations*](https://md.research.elamur.ai/free/en/papers/ge-2024-scaling-synthetic-data-creation-1/)
53. [**Improve Mathematical Reasoning in Language Models by Automated Process Supervision arXiv:2406.06592**Liangchen Luo, Yinxiao Liu, Rosanne Liu, et al.*2024 · 168 citations*](https://md.research.elamur.ai/free/en/papers/luo-2024-improve-mathematical-reasoning-language-models/)
54. **[SCROLLS: Standardized CompaRison Over Long Language Sequences](https://md.research.elamur.ai/free/en/papers/shaham-2022-scrolls-standardized-comparison-over-long/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Uri Shaham, Elad Segal, Maor Ivgi, et al.*2022 · 165 citations*
55. **[CultureLLM: Incorporating Cultural Differences into Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-culturellm-incorporating-cultural-differences-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Cheng Li, Mengzhou Chen, Jindong Wang, et al.*2024 · 162 citations*
56. **[A Comprehensive Survey of Small Language Models in the Era of Large Language Models: Techniques, Enhancements, Applications, Collaboration with LLMs, and Trustworthiness](https://md.research.elamur.ai/free/en/papers/wang-2024-comprehensive-survey-small-language-models/) [ACM Transactions on Intelligent Systems and Technology](https://md.research.elamur.ai/free/en/venues/acm-transactions-on-intelligent-systems-and-technology/)**Fali Wang, Zhiwei Zhang, Xianren Zhang, et al.*2024 · 159 citations*
57. **[Iterative Reasoning Preference Optimization](https://md.research.elamur.ai/free/en/papers/pang-2024-iterative-reasoning-preference-optimization/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Richard Yuanzhe Pang, Weizhe Yuan, Kyunghyun Cho, et al.*2024 · 153 citations*
58. [**Unsolved Problems in ML Safety arXiv:2109.13916**Dan Hendrycks, Nicholas Carlini, John Schulman, et al.*2021 · 151 citations*](https://md.research.elamur.ai/free/en/papers/hendrycks-2021-unsolved-problems-ml-safety/)
59. [**rStar-Math: Small LLMs Can Master Math Reasoning with Self-Evolved Deep Thinking arXiv:2501.04519**Xinyu Guan, Li Lyna Zhang, Yifei Liu, et al.*2025 · 146 citations*](https://md.research.elamur.ai/free/en/papers/guan-2025-rstar-math-small-llms-can/)
60. **[How Abilities in Large Language Models are Affected by Supervised Fine-tuning Data Composition](https://md.research.elamur.ai/free/en/papers/dong-2024-how-abilities-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Guanting Dong, Hongyi Yuan, Keming Lu, et al.*2024 · 145 citations*
61. **[Efficient Methods for Natural Language Processing: A Survey](https://md.research.elamur.ai/free/en/papers/treviso-2023-efficient-methods-natural-language-processing/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Marcos Treviso, Ji-Ung Lee, Tianchu Ji, et al.*2023 · 145 citations*
62. [**JudgeLM: Fine-tuned Large Language Models are Scalable Judges arXiv:2310.17631**Lianghui Zhu, Xinggang Wang, Xinlong Wang*2023 · 145 citations*](https://md.research.elamur.ai/free/en/papers/zhu-2023-judgelm-fine-tuned-large-language/)
63. [**DeepInception: Hypnotize Large Language Model to Be Jailbreaker arXiv:2311.03191**Xuan Li, Zhanke Zhou, Jianing Zhu, et al.*2023 · 141 citations*](https://md.research.elamur.ai/free/en/papers/li-2023-deepinception-hypnotize-large-language-model/)
64. [**DeepSeek-VL2: Mixture-of-Experts Vision-Language Models for Advanced Multimodal Understanding arXiv:2412.10302**Zhiyu Wu, Xiaokang Chen, Zizheng Pan, et al.*2024 · 137 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-deepseek-vl2-mixture-experts/)
65. **[ReFT: Reasoning with Reinforced Fine-Tuning](https://md.research.elamur.ai/free/en/papers/trung-2024-reft-reasoning-reinforced-fine-tuning/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Luong Trung, Xinbo Zhang, Zhanming Jie, et al.*2024 · 134 citations*
66. [**OpenWebMath: An Open Dataset of High-Quality Mathematical Web Text arXiv:2310.06786**Keiran Paster, Marco Dos Santos, Zhangir Azerbayev, et al.*2023 · 132 citations*](https://md.research.elamur.ai/free/en/papers/paster-2023-openwebmath-open-dataset-high-quality/)
67. **[LM vs LM: Detecting Factual Errors via Cross Examination](https://md.research.elamur.ai/free/en/papers/cohen-2023-lm-vs-lm-detecting-factual/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Roi Cohen, May Hamri, Mor Geva, et al.*2023 · 126 citations*
68. [**H2O: Heavy-Hitter Oracle for Efficient Generative Inference of Large Language Models arXiv:2306.14048**Zhenyu Zhang, Ying Sheng, Tianyi Zhou, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/zhang-2023-h-2-o-heavy-hitter/)
69. [**Large Language Model Alignment: A Survey arXiv:2309.15025**Tianhao Shen, Renren Jin, Yufei Huang, et al.*2023 · 124 citations*](https://md.research.elamur.ai/free/en/papers/shen-2023-large-language-model-alignment-survey/)
70. [**LongNet: Scaling Transformers to 1,000,000,000 Tokens arXiv:2307.02486**Jiayu Ding, Shuming Ma, Li Dong, et al.*2023 · 123 citations*](https://md.research.elamur.ai/free/en/papers/ding-2023-longnet-scaling-transformers-1-000/)
71. [**Inference Scaling Laws: An Empirical Analysis of Compute-Optimal Inference for Problem-Solving with Language Models arXiv:2408.00724**Yangzhen Wu, Zhiqing Sun, Shanda Li, et al.*2024 · 122 citations*](https://md.research.elamur.ai/free/en/papers/wu-2024-inference-scaling-laws-empirical-analysis/)
72. [**The Alignment Problem from a Deep Learning Perspective arXiv:2209.00626**Richard Ngo, Lawrence Chan, Soren Mindermann*2022 · 121 citations*](https://md.research.elamur.ai/free/en/papers/ngo-2022-alignment-problem-deep-learning-perspective/)
73. **[AlphaMath Almost Zero: Process Supervision without Process](https://md.research.elamur.ai/free/en/papers/chen-2024-alphamath-almost-zero-process-supervision/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guoxin Chen, Minpeng Liao, Chengxi Li, et al.*2024 · 120 citations*
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