---
title: "NeurIPS 2022 — accepted papers — Elamur Research"
description: "NeurIPS 2022 — accepted papers: 17 papers in the Elamur Research library, with our summaries, ranked by citations."
canonical: "https://research.elamur.ai/free/en/venues/neural-information-processing-systems/2022/"
---
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# NeurIPS 2022 — accepted papers

NeurIPS 2022 is the 2022 edition of the Conference on Neural Information Processing Systems (NeurIPS). OpenAlex lists 2,834 papers of it; our library holds 17 with our summaries. Below: the most cited papers, every accepted paper, and ours with their topics, authors and institutions.

[**2,834** papers in the proceedings](#all) · [**17** works in our library](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2022/#works) · [**10,783** citations of these works](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2022/#works)

## Most cited papers

By their OpenAlex citations; a paper with our summary links to its page here, the others to the original.

1. **[Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models](https://doi.org/10.52202/068431-1800)**Jason Wei, Xuezhi Wang, Dale Schuurmans et al.*2,547 citations*
2. **[Large Language Models Are Zero-Shot Reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/)**Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid et al.*1,557 citations · our summary*
3. **[Training Language Models to Follow Instructions with Human Feedback](https://doi.org/10.52202/068431-2011)**Long Ouyang, Jeffrey Wu, Xu Jiang et al.*1,387 citations*
4. **[The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games](https://doi.org/10.52202/068431-1787)**Chao Yu, Akash Velu, Eugene Vinitsky et al.*1,027 citations*
5. **[FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://doi.org/10.52202/068431-1189)**Tri Dao, Daniel Y. Fu, Stefano Ermon et al.*1,020 citations*
6. **[Flamingo: A Visual Language Model for Few-Shot Learning](https://doi.org/10.52202/068431-1723)**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc et al.*894 citations*
7. **[Training Compute-Optimal Large Language Models](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/)**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch et al.*832 citations · our summary*
8. **[Why Do Tree-Based Models Still Outperform Deep Learning on Typical Tabular Data?](https://doi.org/10.52202/068431-0037)**Léo Grinsztajn, Edouard Oyallon, Gael P. Varoquaux*759 citations*
9. **[VideoMAE: Masked Autoencoders Are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://doi.org/10.52202/068431-0732)**Zhan Tong, Zhan Tong, Jue Wang et al.*725 citations*
10. **[SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://doi.org/10.52202/068431-0084)**Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou et al.*657 citations*

## All accepted papers (2,834)

The most cited first; 17 with our summary link to their page here, the others to the original.

1. **[Chain-Of-Thought Prompting Elicits Reasoning in Large Language Models](https://doi.org/10.52202/068431-1800)**Jason Wei, Xuezhi Wang, Dale Schuurmans et al.*2,547 citations*
2. **[Large Language Models Are Zero-Shot Reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/)**Takeshi Kojima, Shixiang (Shane) Gu, Machel Reid et al.*1,557 citations · our summary*
3. **[Training Language Models to Follow Instructions with Human Feedback](https://doi.org/10.52202/068431-2011)**Long Ouyang, Jeffrey Wu, Xu Jiang et al.*1,387 citations*
4. **[The Surprising Effectiveness of PPO in Cooperative Multi-Agent Games](https://doi.org/10.52202/068431-1787)**Chao Yu, Akash Velu, Eugene Vinitsky et al.*1,027 citations*
5. **[FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness](https://doi.org/10.52202/068431-1189)**Tri Dao, Daniel Y. Fu, Stefano Ermon et al.*1,020 citations*
6. **[Flamingo: A Visual Language Model for Few-Shot Learning](https://doi.org/10.52202/068431-1723)**Jean-Baptiste Alayrac, Jeff Donahue, Pauline Luc et al.*894 citations*
7. **[Training Compute-Optimal Large Language Models](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/)**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch et al.*832 citations · our summary*
8. **[Why Do Tree-Based Models Still Outperform Deep Learning on Typical Tabular Data?](https://doi.org/10.52202/068431-0037)**Léo Grinsztajn, Edouard Oyallon, Gael P. Varoquaux*759 citations*
9. **[VideoMAE: Masked Autoencoders Are Data-Efficient Learners for Self-Supervised Video Pre-Training](https://doi.org/10.52202/068431-0732)**Zhan Tong, Zhan Tong, Jue Wang et al.*725 citations*
10. **[SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation](https://doi.org/10.52202/068431-0084)**Meng-Hao Guo, Cheng-Ze Lu, Qibin Hou et al.*657 citations*
11. **[Elucidating the Design Space of Diffusion-Based Generative Models](https://doi.org/10.52202/068431-1926)**Tero Karras, Miika Aittala, Timo Aila et al.*538 citations*
12. **[Photorealistic Text-To-Image Diffusion Models with Deep Language Understanding](https://doi.org/10.52202/068431-2643)**Chitwan Saharia, William Chan, Saurabh Saxena et al.*530 citations*
13. **[ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation](https://doi.org/10.52202/068431-2795)**Yufei Xu, Jing Zhang, Qiming Zhang et al.*501 citations*
14. **[Non-Stationary Transformers: Exploring the Stationarity in Time Series Forecasting](https://doi.org/10.52202/068431-0718)**Yong Liu, Haixu Wu, Jianmin Wang et al.*497 citations*
15. **[DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps](https://doi.org/10.52202/068431-0418)**Cheng Lu, Yuhao Zhou, Fan Bao et al.*464 citations*
16. **[AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition](https://doi.org/10.52202/068431-1212)**Shoufa Chen, Chongjian Ge, Zhan Tong et al.*451 citations*
17. **[Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/)**Haokun Liu, Derek Tam, Mohammed Muqeeth et al.*377 citations · our summary*
18. **[Learn to Explain: Multimodal Reasoning Via Thought Chains for Science Question Answering](https://doi.org/10.52202/068431-0182)**Pan Lu, Swaroop Ranjan Mishra, Tanglin Xia et al.*362 citations*
19. **[EfficientFormer: Vision Transformers at MobileNet Speed](https://doi.org/10.52202/068431-0940)**Yanyu Li, Geng Yuan, Yang Wen et al.*351 citations*
20. **[Denoising Diffusion Restoration Models](https://doi.org/10.52202/068431-1714)**Bahjat Kawar, Michael Elad, Stefano Ermon et al.*349 citations*
21. **[SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction](https://doi.org/10.52202/068431-0421)**Minhao Liu, Ailing Zeng, Muxi Chen et al.*339 citations*
22. **[GhostNetV2: Enhance Cheap Operation with Long-Range Attention](https://doi.org/10.52202/068431-0724)**Yehui Tang, Kai Han, Jianyuan Guo et al.*336 citations*
23. **[Solving Quantitative Reasoning Problems with Language Models](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/)**Aitor Lewkowycz, Anders Andreassen, David Dohan et al.*328 citations · our summary*
24. **[Recipe for a General, Powerful, Scalable Graph Transformer](https://doi.org/10.52202/068431-1054)**Ladislav Rampášek, Michael Galkin, Vijay Prakash Dwivedi et al.*321 citations*
25. **[LAION-5B: An Open Large-Scale Dataset for Training Next Generation Image-Text Models](https://doi.org/10.52202/068431-1833)**Christoph G. Schuhmann, Romain Beaumont, Richard Vencu et al.*309 citations*

[See all 2,834 papers in NeurIPS 2022 — accepted papers →](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2022/papers/)

## Papers with our summary in our library (17)

The most cited first (OpenAlex citations plus our library's).

1. **[Large language models are zero-shot reasoners](https://md.research.elamur.ai/free/en/papers/kojima-2022-large-language-models-zero-shot/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Takeshi Kojima, Shixiang Shane Gu, Machel Reid, et al.*2022 · 3,795 citations*
2. **[Training Compute-Optimal Large Language Models](https://md.research.elamur.ai/free/en/papers/hoffmann-2022-training-compute-optimal-large-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jordan Hoffmann, Sebastian Borgeaud, Arthur Mensch, et al.*2022 · 1,950 citations*
3. **[Solving Quantitative Reasoning Problems with Language Models](https://md.research.elamur.ai/free/en/papers/lewkowycz-2022-solving-quantitative-reasoning-problems-language/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Aitor Lewkowycz, Anders Andreassen, David Dohan, et al.*2022 · 975 citations*
4. [**STaR: Bootstrapping Reasoning With Reasoning**Eric Zelikman, Yuhuai Wu, Jesse Mu, et al.*2022 · 743 citations*](https://md.research.elamur.ai/free/en/papers/zelikman-2022-star-bootstrapping-reasoning-reasoning/)
5. **[Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://md.research.elamur.ai/free/en/papers/liu-2022-few-shot-parameter-efficient-fine/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Haokun Liu, Derek Tam, Mohammed Muqeeth, et al.*2022 · 736 citations*
6. **[WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents](https://md.research.elamur.ai/free/en/papers/yao-2022-webshop-towards-scalable-real-world/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Shunyu Yao, Howard Chen, John Yang, et al.*2022 · 387 citations*
7. **[ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers](https://md.research.elamur.ai/free/en/papers/yao-2022-zeroquant-efficient-affordable-post-training/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Zhewei Yao, Reza Yazdani Aminabadi, Minjia Zhang, et al.*2022 · 302 citations*
8. **[CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning](https://md.research.elamur.ai/free/en/papers/le-2022-coderl-mastering-code-generation-through/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Hung Le, Yue Wang, Akhilesh Deepak Gotmare, et al.*2022 · 291 citations*
9. **[Beyond neural scaling laws: beating power law scaling via data pruning](https://md.research.elamur.ai/free/en/papers/sorscher-2022-neural-scaling-laws-beating-power/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ben Sorscher, Robert Geirhos, Shashank Shekhar, et al.*2022 · 220 citations*
10. **[Transformer Memory as a Differentiable Search Index](https://md.research.elamur.ai/free/en/papers/tay-2022-transformer-memory-differentiable-search-index/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Yi Tay, Vinh Q. Tran, Mostafa Dehghani, et al.*2022 · 220 citations*
11. **[PlanBench: An Extensible Benchmark for Evaluating Large Language Models on Planning and Reasoning about Change](https://md.research.elamur.ai/free/en/papers/valmeekam-2022-planbench-extensible-benchmark-evaluating-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Karthik Valmeekam, Matthew Marquez, Alberto Olmo, et al.*2022 · 209 citations*
12. **[The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning](https://md.research.elamur.ai/free/en/papers/ye-2022-unreliability-explanations-few-shot-prompting/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Xi Ye, Greg Durrett*2022 · 196 citations*
13. **[Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models](https://md.research.elamur.ai/free/en/papers/tirumala-2022-memorization-overfitting-analyzing-training-dynamics/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Kushal Tirumala, Aram H. Markosyan, Luke Zettlemoyer, et al.*2022 · 183 citations*
14. **[RealTime QA: What's the Answer Right Now?](https://md.research.elamur.ai/free/en/papers/kasai-2022-realtime-qa-s-answer-right/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Jungo Kasai, Keisuke Sakaguchi, Yoichi Takahashi, et al.*2022 · 176 citations*
15. **[Quark: Controllable Text Generation with Reinforced Unlearning](https://md.research.elamur.ai/free/en/papers/lu-2022-quark-controllable-text-generation-reinforced/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Ximing Lu, Sean Welleck, Jack Hessel, et al.*2022 · 165 citations*
16. **[HyperTree Proof Search for Neural Theorem Proving](https://md.research.elamur.ai/free/en/papers/lample-2022-hypertree-proof-search-neural-theorem/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Guillaume Lample, Marie-Anne Lachaux, Thibaut Lavril, et al.*2022 · 149 citations*
17. **[The Goldilocks of Pragmatic Understanding: Fine-Tuning Strategy Matters for Implicature Resolution by LLMs](https://md.research.elamur.ai/free/en/papers/ruis-2022-goldilocks-pragmatic-understanding-fine-tuning/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Laura Ruis, Akbir Khan, Stella Biderman, et al.*2022 · 86 citations*

## Top topics

Topics of its works in our library, with the number of works on each.

[Language models & pretraining (7)](https://md.research.elamur.ai/free/en/topics/language-models/) · [Reasoning (6)](https://md.research.elamur.ai/free/en/topics/reasoning/) · [Implicature (1)](https://md.research.elamur.ai/free/en/topics/implicature/) · [Question answering (1)](https://md.research.elamur.ai/free/en/topics/question-answering/) · [Text generation (1)](https://md.research.elamur.ai/free/en/topics/text-generation/) · [Evaluation & benchmarks (1)](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · [Code & programming languages (1)](https://md.research.elamur.ai/free/en/topics/code-and-programming/) · [Efficient models & compression (1)](https://md.research.elamur.ai/free/en/topics/efficient-models/)

## Fields

[Computer Science (17)](https://md.research.elamur.ai/free/en/shelves/computer-science/) · [Arts and Humanities (14)](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · [Mathematics (1)](https://md.research.elamur.ai/free/en/shelves/mathematics/)

## Top authors

Authors with a page who published here most often: their works here, and the citations of those works.

[🧑‍🔬**Yuhuai Wu**2 works here · 1,718 citations](https://md.research.elamur.ai/free/en/authors/yuhuai-wu/) · [🧑‍🔬**Yejin Choi**2 works here · 341 citations](https://md.research.elamur.ai/free/en/authors/yejin-choi/) · [🧑‍🔬**Machel Reid**1 work here · 3,795 citations](https://md.research.elamur.ai/free/en/authors/machel-reid/) · [🧑‍🔬**Shixiang Shane Gu**1 work here · 3,795 citations](https://md.research.elamur.ai/free/en/authors/shixiang-shane-gu/) · [🧑‍🔬**Takeshi Kojima**1 work here · 3,795 citations](https://md.research.elamur.ai/free/en/authors/takeshi-kojima/) · [🧑‍🔬**Yusuke Iwasawa**1 work here · 3,795 citations](https://md.research.elamur.ai/free/en/authors/yusuke-iwasawa/) · [🧑‍🔬**Yutaka Matsuo**1 work here · 3,795 citations](https://md.research.elamur.ai/free/en/authors/yutaka-matsuo/) · [🧑‍🔬**Aidan Clark**1 work here · 1,950 citations](https://md.research.elamur.ai/free/en/authors/aidan-clark/) · [🧑‍🔬**Arthur Mensch**1 work here · 1,950 citations](https://md.research.elamur.ai/free/en/authors/arthur-mensch/) · [🧑‍🔬**Eliza Rutherford**1 work here · 1,950 citations](https://md.research.elamur.ai/free/en/authors/eliza-rutherford/) · [🧑‍🔬**Johannes Welbl**1 work here · 1,950 citations](https://md.research.elamur.ai/free/en/authors/johannes-welbl/) · [🧑‍🔬**Lisa Anne Hendricks**1 work here · 1,950 citations](https://md.research.elamur.ai/free/en/authors/lisa-anne-hendricks/)

works and citations from OpenAlex and our library.

- [← previous NeurIPS 2021](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2021/)
- [next → NeurIPS 2023](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/2023/)
- [↑ back to NeurIPS](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)