Search Results for author: Albert Q. Jiang

Found 8 papers, 6 papers with code

Multilingual Mathematical Autoformalization

1 code implementation7 Nov 2023 Albert Q. Jiang, Wenda Li, Mateja Jamnik

In this work, we create $\texttt{MMA}$, a large, flexible, multilingual, and multi-domain dataset of informal-formal pairs, by using a language model to translate in the reverse direction, that is, from formal mathematical statements into corresponding informal ones.

Few-Shot Learning Language Acquisition +1

Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs

3 code implementations21 Oct 2022 Albert Q. Jiang, Sean Welleck, Jin Peng Zhou, Wenda Li, Jiacheng Liu, Mateja Jamnik, Timothée Lacroix, Yuhuai Wu, Guillaume Lample

In this work, we introduce Draft, Sketch, and Prove (DSP), a method that maps informal proofs to formal proof sketches, and uses the sketches to guide an automated prover by directing its search to easier sub-problems.

Ranked #3 on Automated Theorem Proving on miniF2F-valid (Pass@100 metric)

Automated Theorem Proving Language Modelling

Autoformalization with Large Language Models

no code implementations25 May 2022 Yuhuai Wu, Albert Q. Jiang, Wenda Li, Markus N. Rabe, Charles Staats, Mateja Jamnik, Christian Szegedy

Autoformalization is the process of automatically translating from natural language mathematics to formal specifications and proofs.

 Ranked #1 on Automated Theorem Proving on miniF2F-test (using extra training data)

Automated Theorem Proving Program Synthesis

Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers

no code implementations22 May 2022 Albert Q. Jiang, Wenda Li, Szymon Tworkowski, Konrad Czechowski, Tomasz Odrzygóźdź, Piotr Miłoś, Yuhuai Wu, Mateja Jamnik

Thor increases a language model's success rate on the PISA dataset from $39\%$ to $57\%$, while solving $8. 2\%$ of problems neither language models nor automated theorem provers are able to solve on their own.

Automated Theorem Proving

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