Summary of An Empirical Study Of Data Ability Boundary in Llms’ Math Reasoning, by Zui Chen et al.
An Empirical Study of Data Ability Boundary in LLMs’ Math Reasoning
by Zui Chen, Yezeng Chen, Jiaqi Han, Zhijie Huang, Ji Qi, Yi Zhou
First submitted to arxiv on: 23 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 This paper explores strategies for enhancing the math reasoning abilities of large language models (LLMs) through supervised fine-tuning. The authors propose a general data strategy to optimize and expand math reasoning capabilities by identifying minimal optimal sets of data paths. They validate that different model abilities can be cumulatively enhanced using a mix of these datasets, achieving state-of-the-art performance on series-based models with lower construction costs. Additionally, the paper highlights the robustness of modern LLMs in numerical tasks and provides an auto problem generator for robustness testing and educational applications. The authors’ approach uses open-source models and publicly available code and data at GitHub. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper is about making computers better at math problems. It’s trying to figure out how to make computer programs learn more about math by using special training data. The researchers found that by giving these programs the right kind of information, they can get much better at doing math quickly and accurately. They also showed that these programs are actually pretty good at doing math now, and it’s not as hard as people thought. To help other scientists test their own programs, the authors created a special tool to generate math problems. All of this work is open-source, which means anyone can use it for free. |
Keywords
* Artificial intelligence * Fine tuning * Supervised
