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Many promising-looking ideas in AI research fail to deliver, but their validation takes substantial human labor and compute.
Neurips 2021 summary, 2021
None · 2021
Earlier work this paper cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
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Forecasting future world events with neural networks
Andy Zou, Tristan Xiao, Ryan Jia, Joe Kwon, Mantas Mazeika, Richard Li, Dawn Song, Jacob Steinhardt, Owain Evans, and Dan Hendrycks · 2022
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Alpacafarm: A simulation framework for methods that learn from human feedback
Yann Dubois, Chen Xuechen Li, Rohan Taori, Tianyi Zhang, Ishaan Gulrajani, Jimmy Ba, Carlos Guestrin, Percy S Liang, and Tatsunori B Hashimoto · 2023
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Mlagentbench: Evaluating language agents on machine learning experimentation
Qian Huang, Jian Vora, Percy Liang, and Jure Leskovec · 2023
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Automatic prompt optimization with" gradient descent" and beam search
Reid Pryzant, Dan Iter, Jerry Li, Yin Tat Lee, Chenguang Zhu, and Michael Zeng · 2023
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Large language model prediction capabilities: Evidence from a real-world forecasting tournament
Philipp Schoenegger and Peter S. Park · 2023
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Goal driven discovery of distributional differences via language descriptions
Ruiqi Zhong, Peter Zhang, Steve Li, Jinwoo Ahn, Dan Klein, and Jacob Steinhardt · 2023
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Humans vs large language models: Judgmental forecasting in an era of advanced ai
Mahdi Abolghasemi, Odkhishig Ganbold, and Kristian Rotaru · 2024
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Litllm: A toolkit for scientific literature review
Shubham Agarwal, Issam H. Laradji, Laurent Charlin, and Christopher Pal · 2024
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Approaching human-level forecasting with language models
Danny Halawi, Fred Zhang, Yueh-Han Chen, and Jacob Steinhardt · 2024
Cited alongside, same era.
Can large language models provide useful feedback on research papers? a large-scale empirical analysis
Can llms generate novel research ideas? a large-scale human study with 100+ nlp researchers
Chenglei Si, Diyi Yang, and Tatsunori Hashimoto · 2024
Later among the works it cites.
Star: Self-taught reasoner bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah D Goodman · 2024
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Juraj Gottweis, Wei-Hung Weng, Alexander Daryin, Tao Tu, Anil Palepu, Petar Sirkovic, Artiom Myaskovsky, Felix Weissenberger, Keran Rong, Ryutaro Tanno, Khaled Saab, Dan Popovici, Jacob Blum, Fan Zhang, Katherine Chou, Avinatan Hassidim, Burak Gokturk, Amin Vahdat, Pushmeet Kohli, Yossi Matias, Andrew Carroll, Kavita Kulkarni, Nenad Tomasev, Vikram Dhillon, Eeshit Dhaval Vaishnav, Byron Lee, Tiago R D Costa, José R Penadés, Gary Peltz, Yunhan Xu, Annalisa Pawlosky, Alan Karthikesalingam, and Vivek Natarajan · 2025
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Forecastbench: A dynamic benchmark of ai forecasting capabilities
Ezra Karger, Houtan Bastani, Yueh-Han Chen, Zachary Jacobs, Danny Halawi, Fred Zhang, and Philip E. Tetlock · 2025
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Weixin Liang, Yuhui Zhang, Hancheng Cao, Binglu Wang, Daisy Yi Ding, Xinyu Yang, Kailas Vodrahalli, Siyu He, Daniel Scott Smith, Yian Yin, et al · 2024
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The ai scientist: Towards fully automated open-ended scientific discovery
Chris Lu, Cong Lu, Robert Tjarko Lange, Jakob Foerster, Jeff Clune, and David Ha · 2024
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Large language models surpass human experts in predicting neuroscience results
Xiaoliang Luo, Akilles Rechardt, Guangzhi Sun, Kevin K Nejad, Felipe Yáñez, Bati Yilmaz, Kangjoo Lee, Alexandra O Cohen, Valentina Borghesani, Anton Pashkov, et al · 2025
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The ai scientist-v2: Workshop-level automated scientific discovery via agentic tree search
Yutaro Yamada, Robert Tjarko Lange, Cong Lu, Shengran Hu, Chris Lu, Jakob Foerster, Jeff Clune, and David Ha · 2025
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