Albert Q. Jiang
Albert Q. Jiang
Other namesAlbert Jiang, Albert Qiaochu Jiang
University of Cambridge | Mistral AI
Verified email at - Homepage
Cited by
Cited by
Mistral 7B
AQ Jiang, A Sablayrolles, A Mensch, C Bamford, DS Chaplot, D Casas, ...
arXiv preprint arXiv:2310.06825, 2023
Mixtral of experts
AQ Jiang, A Sablayrolles, A Roux, A Mensch, B Savary, C Bamford, ...
arXiv preprint arXiv:2401.04088, 2024
Autoformalization with large language models
Y Wu, AQ Jiang, W Li, MN Rabe, C Staats, M Jamnik, C Szegedy
NeurIPS 2022, 2022
Draft, sketch, and prove: Guiding formal theorem provers with informal proofs
AQ Jiang, S Welleck, JP Zhou, W Li, J Liu, M Jamnik, T Lacroix, Y Wu, ...
arXiv preprint arXiv:2210.12283, 2022
Llemma: An open language model for mathematics
Z Azerbayev, H Schoelkopf, K Paster, MD Santos, S McAleer, AQ Jiang, ...
arXiv preprint arXiv:2310.10631, 2023
Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers
AQ Jiang, W Li, S Tworkowski, K Czechowski, T Odrzygóźdź, P Miłoś, ...
NeurIPS 2022, 2022
INT: An Inequality Benchmark for Evaluating Generalization in Theorem Proving
Y Wu, AQ Jiang, J Ba, R Grosse
International Conference on Learning Representations (ICLR 2021), 2021
LISA: Language models of isabelle proofs
AQ Jiang, W Li, JM Han, Y Wu
6th Conference on Artificial Intelligence and Theorem Proving, 2021
Magnushammer: A transformer-based approach to premise selection
M Mikuła, S Antoniak, S Tworkowski, AQ Jiang, JP Zhou, C Szegedy, ...
arXiv preprint arXiv:2303.04488, 2023
Evaluating language models for mathematics through interactions
KM Collins, AQ Jiang, S Frieder, L Wong, M Zilka, U Bhatt, T Lukasiewicz, ...
arXiv preprint arXiv:2306.01694, 2023
Multilingual mathematical autoformalization
AQ Jiang, W Li, M Jamnik
arXiv preprint arXiv:2311.03755, 2023
Learning plausible and useful conjectures
AQ Jiang, W Li, M Jamnik
7th Conference on Artificial Intelligence and Theorem Proving, 2022
Can Network Flatness Explain the Training Speed-Generalisation Connection?
AQ Jiang, L Schut, C Lyle, Y Gal
Bayesian Deep Learning Workshop at the Thirty-fifth Conference on Neural …, 2021
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Articles 1–13