A tutorial on neural theorem proving

Neural theorem proving combines neural language models with formal proof assistants.
This tutorial introduces two research threads in neural theorem proving via interactive Jupyter notebooks.

Part I : Next-step suggestion

Builds a neural next-step suggestion tool, introducing concepts and past work in neural theorem proving along the way.

0. Intronotebook
1. Datanotebook
2. Learningnotebook
3. Proof Searchnotebook
4. Evaluationnotebook
5. llmsuggestnotebook

All notebooks are in (partI_nextstep/notebooks). See partI_nextstep/ntp_python and partI_nextstep/ntp_lean for the Python and Lean files covered in the notebooks.


Please follow the setup instructions in partI_nextstep/README.md.

Part II : Language cascades

Chain together language models to guide formal proof search with informal proofs.

1. Language model cascadesnotebook
2. Draft, Sketch, Provenotebook

All notebooks are in (partII_dsp/notebooks).


Please follow the setup instructions in partII_dsp/README.md.


These materials were originally developed as part of a IJCAI 2023 tutorial.
Slides for the 1 hour summary presentation given at IJCAI 2023 are here.


If you find this tutorial or repository useful in your work, please cite:

  author = {Sean Welleck},
  title = {Neural theorem proving tutorial},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/wellecks/ntptutorial}},