Summary of Subgoalxl: Subgoal-based Expert Learning For Theorem Proving, by Xueliang Zhao et al.
SubgoalXL: Subgoal-based Expert Learning for Theorem Proving
by Xueliang Zhao, Lin Zheng, Haige Bo, Changran Hu, Urmish Thakker, Lingpeng Kong
First submitted to arxiv on: 20 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces SubgoalXL, a novel approach that combines subgoal-based proofs with expert learning to enhance large language models’ capabilities in formal theorem proving within the Isabelle environment. The approach addresses two critical challenges: scarcity of specialized mathematics and theorem-proving data, and need for improved multi-step reasoning abilities. By optimizing data efficiency and employing subgoal-level supervision, SubgoalXL extracts richer information from limited human-generated proofs. The framework integrates subgoal-oriented proof strategies with an expert learning system, iteratively refining formal statement, proof, and subgoal generators. SubgoalXL achieves a new state-of-the-art performance of 56.1% in Isabelle on the standard miniF2F dataset, marking an absolute improvement of 4.9%. The results underscore the effectiveness of maximizing limited data utility and employing targeted guidance for complex reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Formal theorem proving has seen renewed interest with advancements in large language models (LLMs). This paper introduces a new approach that combines subgoal-based proofs with expert learning to enhance LLMs’ capabilities. The approach addresses two critical challenges: scarcity of specialized mathematics and theorem-proving data, and need for improved multi-step reasoning abilities. The results show that this approach can achieve state-of-the-art performance on the miniF2F dataset. |
