Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving

10 April 2024

Papers citing "Learn from Failure: Fine-Tuning LLMs with Trial-and-Error Data for Intuitionistic Propositional Logic Proving"

6 / 6 papers shown

Title
Rango: Adaptive Retrieval-Augmented Proving for Automated Software Verification Kyle Thompson Nuno Saavedra Pedro Carrott Kevin Fisher Alex Sanchez-Stern Yuriy Brun J. Ferreira Sorin Lerner E. First LRM 96 1 0 18 Dec 2024
Can Transformers Do Enumerative Geometry? Baran Hashemi Roderic G. Corominas Alessandro Giacchetto 32 2 0 27 Aug 2024
Magnushammer: A Transformer-Based Approach to Premise Selection Maciej Mikuła Szymon Tworkowski Szymon Antoniak Bartosz Piotrowski Albert Qiaochu Jiang Jinyi Zhou Christian Szegedy Lukasz Kuciñski Piotr Milo's Yuhuai Wu 37 41 0 08 Mar 2023
Draft, Sketch, and Prove: Guiding Formal Theorem Provers with Informal Proofs Albert Q. Jiang Sean Welleck Jin Peng Zhou Wenda Li Jiacheng Liu M. Jamnik Timothée Lacroix Yuhuai Wu Guillaume Lample AIMat 58 154 0 21 Oct 2022
Complexity-Based Prompting for Multi-Step Reasoning Yao Fu Hao-Chun Peng Ashish Sabharwal Peter Clark Tushar Khot ReLM LRM 152 298 0 03 Oct 2022
Self-Consistency Improves Chain of Thought Reasoning in Language Models Xuezhi Wang Jason W. Wei Dale Schuurmans Quoc Le Ed H. Chi Sharan Narang Aakanksha Chowdhery Denny Zhou ReLM BDL LRM AI4CE 297 3,163 0 21 Mar 2022