Fetching the paper…
Reading the bibliography…
The progress of humanity is driven by those successful discoveries accompanied by countless failed experiments.
The Wealth of Nations [1776]
Adam Smith · 1937
Earlier work this paper cites.
A theory of economic development
Gustav Ranis and John C. H. Fei · 1961
Earlier work this paper cites.
The structure of scientific revolutions
Dudley Shapere · 1964
Earlier work this paper cites.
Technological Revolutions and Financial Capital
Carlota Perez · 2003
Earlier work this paper cites.
The Logic of Scientific Discovery
Karl Popper · 2005
Earlier work this paper cites.
Distributed representations of words and phrases and their compositionality
Tomas Mikolov, Ilya Sutskever, Kai Chen, Greg S Corrado, and Jeff Dean · 2013
Earlier work this paper cites.
The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies
Erik Brynjolfsson and Andrew McAfee · 2014
Earlier work this paper cites.
Holophrasm: a neural automated theorem prover for higher-order logic, 2016
Daniel Whalen · 2016
Earlier work this paper cites.
Automating drug discovery
Gisbert Schneider · 2017
Earlier work this paper cites.
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina N. Toutanova · 2018
Earlier work this paper cites.
Did the Model Understand the Question?
Pramod Kaushik Mudrakarta, Ankur Taly, Mukund Sundararajan, and Kedar Dhamdhere · 2018
Earlier work this paper cites.
Fast graph representation learning with PyTorch Geometric
Matthias Fey and Jan E. Lenssen · 2019
Earlier work this paper cites.
Pytorch: An imperative style, high-performance deep learning library
Adam Paszke, Sam Gross, Francisco Massa, Adam Lerer, James Bradbury, Gregory Chanan, Trevor Killeen, Zeming Lin, Natalia Gimelshein, Luca Antiga, Alban Desmaison, Andreas Kopf, Edward Yang, Zachary DeVito, Martin Raison, Alykhan Tejani, Sasank Chilamkurthy, Benoit Steiner, Lu Fang, Junjie Bai, and Soumith Chintala · 2019
Earlier work this paper cites.
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
Earlier work this paper cites.
Shortcut Learning in Deep Neural Networks
Robert Geirhos, Jörn-Henrik Jacobsen, Claudio Michaelis, Richard Zemel, Wieland Brendel, Matthias Bethge, and Felix A. Wichmann · 2020
Earlier work this paper cites.
Artificial intelligence for autonomous molecular design: A perspective
Rajendra P. Joshi and Neeraj Kumar · 2021
Earlier work this paper cites.
Large scale learning on non-homophilous graphs: New benchmarks and strong simple methods
Derek Lim, Felix Hohne, Xiuyu Li, Sijia Linda Huang, Vaishnavi Gupta, Omkar Bhalerao, and Ser Nam Lim · 2021
Earlier work this paper cites.
Deep Stable Learning for Out-Of-Distribution Generalization
Xingxuan Zhang, Peng Cui, Renzhe Xu, Linjun Zhou, Yue He, and Zheyan Shen · 2021
Earlier work this paper cites.
Stable Learning Establishes some Common Ground between Causal Inference and Machine Learning
Peng Cui and Susan Athey · 2022
Earlier work this paper cites.
Training language models to follow instructions with human feedback, March 2022
Long Ouyang, Jeff Wu, Xu Jiang, Diogo Almeida, Carroll L. Wainwright, Pamela Mishkin, Chong Zhang, Sandhini Agarwal, Katarina Slama, Alex Ray, John Schulman, Jacob Hilton, Fraser Kelton, Luke Miller, Maddie Simens, Amanda Askell, Peter Welinder, Paul Christiano, Jan Leike, and Ryan Lowe · 2022
Earlier work this paper cites.
Recipe for a General, Powerful, Scalable Graph Transformer
Ladislav Rampášek, Mikhail Galkin, Vijay Prakash Dwivedi, Anh Tuan Luu, Guy Wolf, and Dominique Beaini · 2022
Cited alongside, same era.
Identifying and mitigating spurious correlations for improving robustness in NLP models
Tianlu Wang, Rohit Sridhar, Diyi Yang, and Xuezhi Wang · 2022
Cited alongside, same era.
Emergent abilities of large language models
Barret Zoph, Colin Raffel, Dale Schuurmans, Dani Yogatama, Denny Zhou, Don Metzler, Ed H. Chi, Jason Wei, Jeff Dean, Liam B. Fedus, Maarten Paul Bosma, Oriol Vinyals, Percy Liang, Sebastian Borgeaud, Tatsunori B. Hashimoto, and Yi Tay · 2022
Cited alongside, same era.
Autonomous chemical research with large language models
Daniil A. Boiko, Robert MacKnight, Ben Kline, and Gabe Gomes · 2023
Cited alongside, same era.
Did the Models Understand Documents? Benchmarking Models for Language Understanding in Document-Level Relation Extraction
Haotian Chen, Bingsheng Chen, and Xiangdong Zhou · 2023
Hugginggpt: Solving ai tasks with chatgpt and its friends in hugging face, 2023
Yongliang Shen, Kaitao Song, Xu Tan, Dongsheng Li, Weiming Lu, and Yueting Zhuang · 2023
Later among the works it cites.
Reflexion: language agents with verbal reinforcement learning
Noah Shinn, Federico Cassano, Ashwin Gopinath, Karthik R Narasimhan, and Shunyu Yao · 2023
Later among the works it cites.
Beyond the imitation game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao, Abu Awal Md Shoeb, Abubakar Abid, Adam Fisch, Adam R. Brown, Adam Santoro, and Aditya Gupta · 2023
Later among the works it cites.
Llama 2: Open foundation and fine-tuned chat models, 2023
Hugo Touvron, Louis Martin, Kevin Stone, Peter Albert, Amjad Almahairi, Yasmine Babaei, Nikolay Bashlykov, Soumya Batra, Prajjwal Bhargava, and Bhosale · 2023
Later among the works it cites.
Revisiting Relation Extraction in the era of Large Language Models
Somin Wadhwa, Silvio Amir, and Byron Wallace · 2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
Anti-symmetric DGN: a stable architecture for deep graph networks
Alessio Gravina, Davide Bacciu, and Claudio Gallicchio · 2023
Cited alongside, same era.
SWE-bench: Can language models resolve real-world GitHub issues?
Carlos E Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan · 2023
Cited alongside, same era.
Swe-bench: Can language models resolve real-world github issues?, 2023
Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao, Kexin Pei, Ofir Press, and Karthik Narasimhan · 2023
Cited alongside, same era.
Theory of mind for multi-agent collaboration via large language models
Huao Li, Yu Quan Chong, Simon Stepputtis, Joseph Campbell, Dana Hughes, Michael Lewis, and Katia P. Sycara · 2023
Cited alongside, same era.
ML-Bench: Large Language Models Leverage Open-source Libraries for Machine Learning Tasks, November 2023
Yuliang Liu, Xiangru Tang, Zefan Cai, Junjie Lu, Yichi Zhang, Yanjun Shao, Zexuan Deng, Helan Hu, Zengxian Yang, Kaikai An, Ruijun Huang, Shuzheng Si, Sheng Chen, Haozhe Zhao, Zhengliang Li, Liang Chen, Yiming Zong, Yan Wang, Tianyu Liu, Zhiwei Jiang, Baobao Chang, Yujia Qin, Wangchunshu Zhou, Yilun Zhao, Arman Cohan, and Mark Gerstein · 2023
Cited alongside, same era.
Ml-bench: Large language models leverage open-source libraries for machine learning tasks, 2023
Yuliang Liu, Xiangru Tang, Zefan Cai, Junjie Lu, Yichi Zhang, Yanjun Shao, Zexuan Deng, Helan Hu, Zengxian Yang, Kaikai An, Ruijun Huang, Shuzheng Si, Sheng Chen, Haozhe Zhao, Zhengliang Li, Liang Chen, Yiming Zong, Yan Wang, Tianyu Liu, Zhiwei Jiang, Baobao Chang, Yujia Qin, Wangchunshu Zhou, Yilun Zhao, Arman Cohan, and Mark Gerstein · 2023
Cited alongside, same era.
A survey of deep learning for mathematical reasoning
Pan Lu, Liang Qiu, Wenhao Yu, Sean Welleck, and Kai-Wei Chang · 2023
Cited alongside, same era.
Voyager: An open-ended embodied agent with large language models, 2023
Guanzhi Wang, Yuqi Xie, Yunfan Jiang, Ajay Mandlekar, Chaowei Xiao, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
Later among the works it cites.
Dt-solver: Automated theorem proving with dynamic-tree sampling guided by proof-level value function
Haiming Wang, Ye Yuan, Zhengying Liu, Jianhao Shen, Yichun Yin, Jing Xiong, Enze Xie, Han Shi, Yujun Li, Lin Li, Jian Yin, Zhenguo Li, and Xiaodan Liang · 2023
Later among the works it cites.
Autogen: Enabling next-gen llm applications via multi-agent conversation, 2023
Qingyun Wu, Gagan Bansal, Jieyu Zhang, Yiran Wu, Beibin Li, Erkang Zhu, Li Jiang, Xiaoyun Zhang, Shaokun Zhang, Jiale Liu, Ahmed Hassan Awadallah, Ryen W White, Doug Burger, and Chi Wang · 2023
Later among the works it cites.
Graph neural networks are inherently good generalizers: Insights by bridging gnns and mlps
Chenxiao Yang, Qitian Wu, Jiahua Wang, and Junchi Yan · 2023
Later among the works it cites.
Auto-gpt for online decision making: Benchmarks and additional opinions, 2023
Hui Yang, Sifu Yue, and Yunzhong He · 2023
Later among the works it cites.
Leandojo: Theorem proving with retrieval-augmented language models
Kaiyu Yang, Aidan M Swope, Alex Gu, Rahul Chalamala, Peiyang Song, Shixing Yu, Saad Godil, Ryan Prenger, and Anima Anandkumar · 2023
Later among the works it cites.
Leveraging large language model for automatic evolving of industrial data-centric r&d cycle, 2023
Xu Yang, Xiao Yang, Weiqing Liu, Jinhui Li, Peng Yu, Zeqi Ye, and Jiang Bian · 2023
Later among the works it cites.
Tree of thoughts: Deliberate problem solving with large language models, 2023
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
Later among the works it cites.
A survey of large language models, 2023
Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, and Ji-Rong Wen · 2023
Later among the works it cites.
ToolQA: A Dataset for LLM Question Answering with External Tools, June 2023
Yuchen Zhuang, Yue Yu, Kuan Wang, Haotian Sun, and Chao Zhang · 2023
Later among the works it cites.
MetaTool benchmark: Deciding whether to use tools and which to use
Anonymous · 2024
Closest in time.
yoheinakajima/babyagi
Franci Penov, Yohei Nakajima, Malik M Alnakhaleh, Alexander Dibrov, Shukri, Frank Chen, Anton Troynikov, David Byttow, John Cao, Felipe Schieber, Josh XT, FRM Minsu Yeom, CFA, Zain Hasan, zeel sheladiya, jmtatsch, Aidan Rauscher, Thiago Alves, jakvb, Jason Banich, Muhamed AlGhzawi, Peter Banda, TungusSs, Lorenzo Fontoura, Joe Heitzeberg, Jay Scambler, Ikko Eltociear Ashimine, Cs4K1Sr4C, Mike Crawford, Michele Bellitti, and swyx.io · 2024
Closest in time.
Debugbench: Evaluating debugging capability of large language models, 2024
Runchu Tian, Yining Ye, Yujia Qin, Xin Cong, Yankai Lin, Yinxu Pan, Yesai Wu, Zhiyuan Liu, and Maosong Sun · 2024
Closest in time.
Enhancing geometric representations for molecules with equivariant vector-scalar interactive message passing
Yusong Wang, Tong Wang, Shaoning Li, Xinheng He, Mingyu Li, Zun Wang, Nanning Zheng, Bin Shao, and Tie-Yan Liu · 2024
Closest in time.