Fetching the paper…
Reading the bibliography…
Large language models (LLMs) have shown promise in proving formal theorems using proof assistants such as Lean.
Learning to reason in large theories without imitation
Kshitij Bansal, Christian Szegedy, Markus N Rabe, Sarah M Loos, and Viktor Toman · 1905
Earlier work this paper cites.
The logic theory machine–a complex information processing system
Allen Newell and Herbert Simon · 1956
Earlier work this paper cites.
The formulae-as-types notion of construction
William A Howard · 1980
Earlier work this paper cites.
The Coq proof assistant reference manual: Version 6.1
Bruno Barras, Samuel Boutin, Cristina Cornes, Judicaël Courant, Jean-Christophe Filliatre, Eduardo Gimenez, Hugo Herbelin, Gerard Huet, Cesar Munoz, Chetan Murthy, et al · 1997
Earlier work this paper cites.
Handbook of automated reasoning , volume 1
Alan JA Robinson and Andrei Voronkov · 2001
Earlier work this paper cites.
Isabelle/HOL: a proof assistant for higher-order logic
Tobias Nipkow, Markus Wenzel, and Lawrence C Paulson · 2002
Earlier work this paper cites.
MPTP—motivation, implementation, first experiments
Josef Urban · 2004
Earlier work this paper cites.
The probabilistic relevance framework: BM25 and beyond
Stephen Robertson, Hugo Zaragoza, et al · 2009
Earlier work this paper cites.
Sledgehammer: judgement day
Sascha Böhme and Tobias Nipkow · 2010
Earlier work this paper cites.
First-order theorem proving and vampire
Laura Kovács and Andrei Voronkov · 2013
Earlier work this paper cites.
Machine learning for first-order theorem proving: learning to select a good heuristic
James P Bridge, Sean B Holden, and Lawrence C Paulson · 2014
Earlier work this paper cites.
Premise selection for mathematics by corpus analysis and kernel methods
Jesse Alama, Tom Heskes, Daniel Kühlwein, Evgeni Tsivtsivadze, and Josef Urban · 2014
Earlier work this paper cites.
CompCert—a formally verified optimizing compiler
Xavier Leroy, Sandrine Blazy, Daniel Kästner, Bernhard Schommer, Markus Pister, and Christian Ferdinand · 2016
Earlier work this paper cites.
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
Earlier work this paper cites.
DeepMath—deep sequence models for premise selection
Geoffrey Irving, Christian Szegedy, Alexander A Alemi, Niklas Eén, François Chollet, and Josef Urban · 2016
Earlier work this paper cites.
Hammering towards QED
Jasmin Christian Blanchette, Cezary Kaliszyk, Lawrence C Paulson, and Josef Urban · 2016
Earlier work this paper cites.
Attention is all you need
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin · 2017
Earlier work this paper cites.
Deep network guided proof search
Sarah Loos, Geoffrey Irving, Christian Szegedy, and Cezary Kaliszyk · 2017
Earlier work this paper cites.
HolStep: A machine learning dataset for higher-order logic theorem proving
Cezary Kaliszyk, François Chollet, and Christian Szegedy · 2017
Earlier work this paper cites.
Hammer for Coq: Automation for dependent type theory
Łukasz Czajka and Cezary Kaliszyk · 2018
Earlier work this paper cites.
First experiments with neural translation of informal to formal mathematics
Qingxiang Wang, Cezary Kaliszyk, and Josef Urban · 2018
Earlier work this paper cites.
Retrieval-based neural code generation
Shirley Anugrah Hayati, Raphael Olivier, Pravalika Avvaru, Pengcheng Yin, Anthony Tomasic, and Graham Neubig · 2018
Earlier work this paper cites.
The future of mathematics
Kevin Buzzard · 2019
Earlier work this paper cites.
QED at large: A survey of engineering of formally verified software
Talia Ringer, Karl Palmskog, Ilya Sergey, Milos Gligoric, Zachary Tatlock, et al · 2019
Earlier work this paper cites.
Learning to prove theorems via interacting with proof assistants
Kaiyu Yang and Jia Deng · 2019
Earlier work this paper cites.
GamePad: A learning environment for theorem proving
Daniel Huang, Prafulla Dhariwal, Dawn Song, and Ilya Sutskever · 2019
Earlier work this paper cites.
Decoupled weight decay regularization
Ilya Loshchilov and Frank Hutter · 2019
Earlier work this paper cites.
Metamath: a computer language for mathematical proofs
Norman Megill and David A Wheeler · 2019
Earlier work this paper cites.
Generative language modeling for automated theorem proving
Stanislas Polu and Ilya Sutskever · 2020
Earlier work this paper cites.
The Lean mathematical library
The mathlib Community · 2020
Earlier work this paper cites.
Dense passage retrieval for open-domain question answering
Vladimir Karpukhin, Barlas Oguz, Sewon Min, Patrick Lewis, Ledell Wu, Sergey Edunov, Danqi Chen, and Wen-tau Yih · 2020
Earlier work this paper cites.
Graph representations for higher-order logic and theorem proving
Aditya Paliwal, Sarah Loos, Markus Rabe, Kshitij Bansal, and Christian Szegedy · 2020
Earlier work this paper cites.
Learning to prove theorems by learning to generate theorems
Mingzhe Wang and Jia Deng · 2020
Cited alongside, same era.
Premise selection in natural language mathematical texts
Deborah Ferreira and André Freitas · 2020
Cited alongside, same era.
Generalization through memorization: Nearest neighbor language models
Urvashi Khandelwal, Omer Levy, Dan Jurafsky, Luke Zettlemoyer, and Mike Lewis · 2020
Cited alongside, same era.
Retrieval augmented language model pre-training
Kelvin Guu, Kenton Lee, Zora Tung, Panupong Pasupat, and Mingwei Chang · 2020
Cited alongside, same era.
Retrieval-augmented generation for knowledge-intensive NLP tasks
Patrick Lewis, Ethan Perez, Aleksandra Piktus, Fabio Petroni, Vladimir Karpukhin, Naman Goyal, Heinrich Küttler, Mike Lewis, Wen-tau Yih, Tim Rocktäschel, et al · 2020
Cited alongside, same era.
Exploring the limits of transfer learning with a unified text-to-text transformer
Few-shot learning with retrieval augmented language models
Gautier Izacard, Patrick Lewis, Maria Lomeli, Lucas Hosseini, Fabio Petroni, Timo Schick, Jane Dwivedi-Yu, Armand Joulin, Sebastian Riedel, and Edouard Grave · 2022
Later among the works it cites.
Training language models with memory augmentation
Zexuan Zhong, Tao Lei, and Danqi Chen · 2022
Later among the works it cites.
Repository-level prompt generation for large language models of code
Disha Shrivastava, Hugo Larochelle, and Daniel Tarlow · 2022
Later among the works it cites.
CoCoMIC: Code completion by jointly modeling in-file and cross-file context
Yangruibo Ding, Zijian Wang, Wasi Uddin Ahmad, Murali Krishna Ramanathan, Ramesh Nallapati, Parminder Bhatia, Dan Roth, and Bing Xiang · 2022
Later among the works it cites.
CodeGen: An open large language model for code with multi-turn program synthesis
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Cited alongside, same era.
DeepSpeed: System optimizations enable training deep learning models with over 100 billion parameters
Jeff Rasley, Samyam Rajbhandari, Olatunji Ruwase, and Yuxiong He · 2020
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
LISA: Language models of ISAbelle proofs
Albert Qiaochu Jiang, Wenda Li, Jesse Michael Han, and Yuhuai Wu · 2021
Cited alongside, same era.
TacticToe: learning to prove with tactics
Thibault Gauthier, Cezary Kaliszyk, Josef Urban, Ramana Kumar, and Michael Norrish · 2021
Cited alongside, same era.
Mathematical reasoning via self-supervised skip-tree training
Markus Norman Rabe, Dennis Lee, Kshitij Bansal, and Christian Szegedy · 2021
Cited alongside, same era.
Retrieval-augmented proof step synthesis
Christian Szegedy, Markus Rabe, and Henryk Michalewski · 2021
Cited alongside, same era.
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2022
Later among the works it cites.
Transformer memory as a differentiable search index
Yi Tay, Vinh Tran, Mostafa Dehghani, Jianmo Ni, Dara Bahri, Harsh Mehta, Zhen Qin, Kai Hui, Zhe Zhao, Jai Gupta, et al · 2022
Later among the works it cites.
Autoregressive search engines: Generating substrings as document identifiers
Michele Bevilacqua, Giuseppe Ottaviano, Patrick Lewis, Scott Yih, Sebastian Riedel, and Fabio Petroni · 2022
Later among the works it cites.
Survey of hallucination in natural language generation
Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Ye Jin Bang, Andrea Madotto, and Pascale Fung · 2023
Closest in time.
Toolformer: Language models can teach themselves to use tools
Timo Schick, Jane Dwivedi-Yu, Roberto Dessì, Roberta Raileanu, Maria Lomeli, Luke Zettlemoyer, Nicola Cancedda, and Thomas Scialom · 2023
Closest in time.
Formal mathematics statement curriculum learning
Stanislas Polu, Jesse Michael Han, Kunhao Zheng, Mantas Baksys, Igor Babuschkin, and Ilya Sutskever · 2023
Closest in time.
Baldur: Whole-proof generation and repair with large language models
Emily First, Markus N Rabe, Talia Ringer, and Yuriy Brun · 2023
Closest in time.
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, et al · 2023
Closest in time.
ProofNet: Autoformalizing and formally proving undergraduate-level mathematics
Zhangir Azerbayev, Bartosz Piotrowski, Hailey Schoelkopf, Edward W Ayers, Dragomir Radev, and Jeremy Avigad · 2023
Closest in time.
Machine-learned premise selection for Lean
Bartosz Piotrowski, Ramon Fernández Mir, and Edward Ayers · 2023
Closest in time.
Magnushammer: A transformer-based approach to premise selection
Maciej Mikuła, Szymon Antoniak, Szymon Tworkowski, Albert Qiaochu Jiang, Jin Peng Zhou, Christian Szegedy, Łukasz Kuciński, Piotr Miłoś, and Yuhuai Wu · 2023
Closest in time.
CoProver: A recommender system for proof construction
Eric Yeh, Briland Hitaj, Sam Owre, Maena Quemener, and Natarajan Shankar · 2023
Closest in time.
Proof repair infrastructure for supervised models: Building a large proof repair dataset
Tom Reichel, R Henderson, Andrew Touchet, Andrew Gardner, and Talia Ringer · 2023
Closest in time.
Matthias Cosler, Christopher Hahn, Daniel Mendoza, Frederik Schmitt, and Caroline Trippel · 2023
Closest in time.
Data-efficient learning of natural language to linear temporal logic translators for robot task specification
Jiayi Pan, Glen Chou, and Dmitry Berenson · 2023
Closest in time.
Draft, Sketch, and Prove: Guiding formal theorem provers with informal proofs
Albert Q Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothée Lacroix, Yuhuai Wu, and Guillaume Lample · 2023
Closest in time.
Decomposing the enigma: Subgoal-based demonstration learning for formal theorem proving
Xueliang Zhao, Wenda Li, and Lingpeng Kong · 2023
Closest in time.
Towards autoformalization of mathematics and code correctness: Experiments with elementary proofs
Garett Cunningham, Razvan C Bunescu, and David Juedes · 2023
Closest in time.
NL2TL: Transforming natural languages to temporal logics using large language models
Yongchao Chen, Rujul Gandhi, Yang Zhang, and Chuchu Fan · 2023
Closest in time.
DocPrompting: Generating code by retrieving the docs
Shuyan Zhou, Uri Alon, Frank F Xu, Zhengbao Jiang, and Graham Neubig · 2023
Closest in time.
RepoCoder: Repository-level code completion through iterative retrieval and generation
Fengji Zhang, Bei Chen, Yue Zhang, Jin Liu, Daoguang Zan, Yi Mao, Jian-Guang Lou, and Weizhu Chen · 2023
Closest in time.
Functional programming in Lean, 2023
David Thrane Christiansen · 2023
Closest in time.
LLaMA: Open and efficient foundation language models
Hugo Touvron, Thibaut Lavril, Gautier Izacard, Xavier Martinet, Marie-Anne Lachaux, Timothée Lacroix, Baptiste Rozière, Naman Goyal, Eric Hambro, Faisal Azhar, et al · 2023
Closest in time.
StarCoder: may the source be with you!
Raymond Li, Loubna Ben Allal, Yangtian Zi, Niklas Muennighoff, Denis Kocetkov, Chenghao Mou, Marc Marone, Christopher Akiki, Jia Li, Jenny Chim, et al · 2023
Closest in time.
Tree of thoughts: Deliberate problem solving with large language models
Shunyu Yao, Dian Yu, Jeffrey Zhao, Izhak Shafran, Thomas L. Griffiths, Yuan Cao, and Karthik Narasimhan · 2023
Closest in time.
CodeGeeX: A pre-trained model for code generation with multilingual evaluations on HumanEval-X
Qinkai Zheng, Xiao Xia, Xu Zou, Yuxiao Dong, Shan Wang, Yufei Xue, Zihan Wang, Lei Shen, Andi Wang, Yang Li, et al · 2023
Closest in time.
MegaByte: Predicting million-byte sequences with multiscale transformers
Lili Yu, Dániel Simig, Colin Flaherty, Armen Aghajanyan, Luke Zettlemoyer, and Mike Lewis · 2023
Closest in time.
How does generative retrieval scale to millions of passages?
Ronak Pradeep, Kai Hui, Jai Gupta, Adam D Lelkes, Honglei Zhuang, Jimmy Lin, Donald Metzler, and Vinh Q Tran · 2023
Closest in time.