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Artificial Intelligence (AI) significantly influences many fields, largely thanks to the vast amounts of high-quality data for machine learning models.
The Fourth Paradigm: Data-intensive Scientific Discovery
Tony Hey, Stewart Tansley, Kristin Tolle, and Jim Gray · 2009
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101 formulaic alphas
Zura Kakushadze · 2016
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BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina N. Toutanova · 2018
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Are Ideas Getting Harder to Find?
Nicholas Bloom, Charles I. Jones, John Van Reenen, and Michael Webb · 2020
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Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel Ziegler, Jeffrey Wu, Clemens Winter, Chris Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Self-critiquing models for assisting human evaluators, June 2022
William Saunders, Catherine Yeh, Jeff Wu, Steven Bills, Long Ouyang, Jonathan Ward, and Jan Leike · 2022
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Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, brian ichter, Fei Xia, Ed Chi, Quoc V Le, and Denny Zhou · 2022
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Emergent autonomous scientific research capabilities of large language models
Daniil A Boiko, Robert MacKnight, and Gabe Gomes · 2023
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Benchmarking large language models as ai research agents
Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec · 2023
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Self-planning code generation with large language model
Xue Jiang, Yihong Dong, Lecheng Wang, Qiwei Shang, and Ge Li · 2023
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Chain of Thought with Explicit Evidence Reasoning for Few-shot Relation Extraction
Xilai Ma, Jing Li, and Min Zhang · 2023
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Augmented language models: A survey
Grégoire Mialon, Roberto Dessi, Maria Lomeli, Christoforos Nalmpantis, Ramakanth Pasunuru, Roberta Raileanu, Baptiste Roziere, Timo Schick, Jane Dwivedi-Yu, Asli Celikyilmaz, Edouard Grave, Yann LeCun, and Thomas Scialom · 2023
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AI can help to speed up drug discovery — but only if we give it the right data
Marissa Mock, Suzanne Edavettal, Christopher Langmead, and Alan Russell · 2023
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GPT-4 Technical Report, March 2023
OpenAI · 2023
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Check Your Facts and Try Again: Improving Large Language Models with External Knowledge and Automated Feedback, March 2023
Baolin Peng, Michel Galley, Pengcheng He, Hao Cheng, Yujia Xie, Yu Hu, Qiuyuan Huang, Lars Liden, Zhou Yu, Weizhu Chen, and Jianfeng Gao · 2023
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Semiparametric Language Models Are Scalable Continual Learners, March 2023
Guangyue Peng, Tao Ge, Si-Qing Chen, Furu Wei, and Houfeng Wang · 2023
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Tool learning with foundation models
Yujia Qin, Shengding Hu, Yankai Lin, Weize Chen, Ning Ding, Ganqu Cui, Zheni Zeng, Yufei Huang, Chaojun Xiao, Chi Han, et al · 2023
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Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessi, Roberta Raileanu, Maria Lomeli, Eric Hambro, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
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Reflexion: Language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik Narasimhan, and Shunyu Yao · 2023
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Voyager: An open-ended embodied agent with large language models
Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
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Scientific discovery in the age of artificial intelligence
Hanchen Wang, Tianfan Fu, Yuanqi Du, Wenhao Gao, Kexin Huang, Ziming Liu, Payal Chandak, Shengchao Liu, Peter Van Katwyk, Andreea Deac, Anima Anandkumar, Karianne Bergen, Carla P. Gomes, Shirley Ho, Pushmeet Kohli, Joan Lasenby, Jure Leskovec, Tie-Yan Liu, Arjun Manrai, Debora Marks, Bharath Ramsundar, Le Song, Jimeng Sun, Jian Tang, Petar Veličković, Max Welling, Linfeng Zhang, Connor W. Coley, Yoshua Bengio, and Marinka Zitnik · 2023
Teaching large language models to self-debug
Xinyun Chen, Maxwell Lin, Nathanael Schärli, and Denny Zhou · 2024
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An agent foundation model
Zane Durante, Bidipta Sarkar, Ran Gong, Rohan Taori, Yusuke Noda, Paul Tang, Ehsan Adeli, Shrinidhi Kowshika Lakshmikanth, Kevin Schulman, Arnold Milstein, Demetri Terzopoulos, Ade Famoti, Noboru Kuno, Ashley J. Llorens, Hoi Vo, Katsushi Ikeuchi, Fei-Fei Li, Jianfeng Gao, Naoki Wake, and Qiuyuan Huang · 2024
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CLOVA: A Closed-Loop Visual Assistant with Tool Usage and Update, March 2024
Zhi Gao, Yuntao Du, Xintong Zhang, Xiaojian Ma, Wenjuan Han, Song-Chun Zhu, and Qing Li · 2024
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CRITIC: Large language models can self-correct with tool-interactive critiquing
Zhibin Gou, Zhihong Shao, Yeyun Gong, yelong shen, Yujiu Yang, Nan Duan, and Weizhu Chen · 2024
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Large language models for automated data science: Introducing caafe for context-aware automated feature engineering
Noah Hollmann, Samuel Müller, and Frank Hutter · 2024
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alphaXiv searches the wider corpus for related work and actual follow-ups.
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Cited alongside, same era.
Lemur: Harmonizing Natural Language and Code for Language Agents, October 2023
Yiheng Xu, Hongjin Su, Chen Xing, Boyu Mi, Qian Liu, Weijia Shi, Binyuan Hui, Fan Zhou, Yitao Liu, Tianbao Xie, Zhoujun Cheng, Siheng Zhao, Lingpeng Kong, Bailin Wang, Caiming Xiong, and Tao Yu · 2023
Cited alongside, same era.
Failures Pave the Way: Enhancing Large Language Models through Tuning-free Rule Accumulation
Zeyuan Yang, Peng Li, and Yang Liu · 2023
Cited alongside, same era.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Tom Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
Cited alongside, same era.
ReAct: Synergizing reasoning and acting in language models
Shunyu Yao, Jeffrey Zhao, Dian Yu, Nan Du, Izhak Shafran, Karthik R Narasimhan, and Yuan Cao · 2023
Cited alongside, same era.
Data-centric artificial intelligence: A survey
Daochen Zha, Zaid Pervaiz Bhat, Kwei-Herng Lai, Fan Yang, Zhimeng Jiang, Shaochen Zhong, and Xia Hu · 2023
Cited alongside, same era.
Mlcopilot: Unleashing the power of large language models in solving machine learning tasks
Lei Zhang, Yuge Zhang, Kan Ren, Dongsheng Li, and Yuqing Yang · 2023
Cited alongside, same era.
Automatic chain of thought prompting in large language models
Zhuosheng Zhang, Aston Zhang, Mu Li, and Alex Smola · 2023
Cited alongside, same era.
Tool-augmented reward modeling
Lei Li, Yekun Chai, Shuohuan Wang, Yu Sun, Hao Tian, Ningyu Zhang, and Hua Wu · 2024
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ChipNeMo: Domain-Adapted LLMs for Chip Design, April 2024
Mingjie Liu, Teodor-Dumitru Ene, Robert Kirby, Chris Cheng, Nathaniel Pinckney, Rongjian Liang, Jonah Alben, Himyanshu Anand, Sanmitra Banerjee, Ismet Bayraktaroglu, Bonita Bhaskaran, Bryan Catanzaro, Arjun Chaudhuri, Sharon Clay, Bill Dally, Laura Dang, Parikshit Deshpande, Siddhanth Dhodhi, Sameer Halepete, Eric Hill, Jiashang Hu, Sumit Jain, Ankit Jindal, Brucek Khailany, George Kokai, Kishor Kunal, Xiaowei Li, Charley Lind, Hao Liu, Stuart Oberman, Sujeet Omar, Ghasem Pasandi, Sreedhar Pratty, Jonathan Raiman, Ambar Sarkar, Zhengjiang Shao, Hanfei Sun, Pratik P. Suthar, Varun Tej, Walker Turner, Kaizhe Xu, and Haoxing Ren · 2024
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SELF: Language-driven self-evolution for large language model, 2024
Jianqiao Lu, Wanjun Zhong, Wenyong Huang, Yufei Wang, Fei Mi, Baojun Wang, Weichao Wang, Lifeng Shang, and Qun Liu · 2024
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ToolLLM: Facilitating large language models to master 16000+ real-world APIs
Yujia Qin, Shihao Liang, Yining Ye, Kunlun Zhu, Lan Yan, Yaxi Lu, Yankai Lin, Xin Cong, Xiangru Tang, Bill Qian, Sihan Zhao, Lauren Hong, Runchu Tian, Ruobing Xie, Jie Zhou, Mark Gerstein, dahai li, Zhiyuan Liu, and Maosong Sun · 2024
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Code Generation with AlphaCodium: From Prompt Engineering to Flow Engineering, January 2024
Tal Ridnik, Dedy Kredo, and Itamar Friedman · 2024
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Report on Recommendations for Supercharging Research: Harnessing Artificial Intelligence to Meet Global Challenges
Terence Tao and Laura H. Greene · 2024
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Autodev: Automated ai-driven development
Michele Tufano, Anisha Agarwal, Jinu Jang, Roshanak Zilouchian Moghaddam, and Neel Sundaresan · 2024
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OS-Copilot: Towards Generalist Computer Agents with Self-Improvement, February 2024
Zhiyong Wu, Chengcheng Han, Zichen Ding, Zhenmin Weng, Zhoumianze Liu, Shunyu Yao, Tao Yu, and Lingpeng Kong · 2024
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CRAFT: Customizing LLMs by creating and retrieving from specialized toolsets
Lifan Yuan, Yangyi Chen, Xingyao Wang, Yi Fung, Hao Peng, and Heng Ji · 2024
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MatterGen: A generative model for inorganic materials design, January 2024
Claudio Zeni, Robert Pinsler, Daniel Zügner, Andrew Fowler, Matthew Horton, Xiang Fu, Sasha Shysheya, Jonathan Crabbé, Lixin Sun, Jake Smith, Bichlien Nguyen, Hannes Schulz, Sarah Lewis, Chin-Wei Huang, Ziheng Lu, Yichi Zhou, Han Yang, Hongxia Hao, Jielan Li, Ryota Tomioka, and Tian Xie · 2024
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Training Language Model Agents without Modifying Language Models, February 2024
Shaokun Zhang, Jieyu Zhang, Jiale Liu, Linxin Song, Chi Wang, Ranjay Krishna, and Qingyun Wu · 2024
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