Summary of Minictx: Neural Theorem Proving with (long-)contexts, by Jiewen Hu et al.
miniCTX: Neural Theorem Proving with (Long-)Contexts
by Jiewen Hu, Thomas Zhu, Sean Welleck
First submitted to arxiv on: 5 Aug 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces miniCTX, a dataset and benchmark for testing models’ ability to prove formal mathematical theorems that rely on context information. The dataset contains theorems sourced from real Lean projects and textbooks, each associated with a context that can span tens of thousands of tokens. Models are tasked with proving a theorem given access to code from the theorem’s repository, which contains necessary context for the proof. Traditional methods relying solely on state information are outperformed by fine-tuning and prompting methods that condition theorem proving on preceding context. miniCTX is shown to be a challenging and realistic evaluation of neural theorem provers. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to test how well computers can prove math theorems, which is important for many real-world applications. The test uses actual math problems from Lean projects and textbooks, along with lots of extra information that’s needed to solve the problem. The goal is to see if computers can use this extra information to help them figure out the proof. So far, using context has been shown to be a better way for computers to prove theorems than just relying on what they already know. |
Keywords
* Artificial intelligence * Fine tuning * Prompting
