Summary of Data For Mathematical Copilots: Better Ways Of Presenting Proofs For Machine Learning, by Simon Frieder et al.
Data for Mathematical Copilots: Better Ways of Presenting Proofs for Machine Learning
by Simon Frieder, Jonas Bayer, Katherine M. Collins, Julius Berner, Jacob Loader, András Juhász, Fabian Ruehle, Sean Welleck, Gabriel Poesia, Ryan-Rhys Griffiths, Adrian Weller, Anirudh Goyal, Thomas Lukasiewicz, Timothy Gowers
First submitted to arxiv on: 19 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 A comprehensive evaluation suite is necessary for advancing AI-based mathematical assistants, as current datasets exhibit significant limitations. Large language models primarily trained on lower-level mathematics and binary rating protocols hinder proof-based evaluation. This study argues that a paradigm shift in dataset design and evaluation criteria is essential. By moving away from result-based datasets and incorporating mathematical workflows and motivated proofs, we can provide a better learning signal for large language models. We introduce math datasheets as a questionnaire for creators to assess potential limitations and promote transparency. The proposed changes will facilitate the development of more comprehensive and effective mathematical copilots. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary AI assistants need better training data to improve their math skills. Current datasets have flaws, such as only covering simple math problems and using easy-to-understand answers. This makes it hard to test how well AI can solve complex math proofs. To fix this, we suggest changing the way datasets are designed and evaluated. We propose incorporating more realistic math workflows and proof examples that show the thought process behind solving a problem. Additionally, creators should provide more information about their datasets so users can better understand their limitations. |
