Summary of Openmathinstruct-2: Accelerating Ai For Math with Massive Open-source Instruction Data, by Shubham Toshniwal et al.
OpenMathInstruct-2: Accelerating AI for Math with Massive Open-Source Instruction Data
by Shubham Toshniwal, Wei Du, Ivan Moshkov, Branislav Kisacanin, Alexan Ayrapetyan, Igor Gitman
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 The paper addresses the challenge of mathematical reasoning in large language models (LLMs), which has seen significant progress but is often inaccessible due to proprietary training data. The authors conduct ablation experiments on data synthesis using the Llama3.1 family of models, exploring factors such as solution format, teacher-student model interactions, and question diversity. Their findings highlight the importance of precise solutions, robustness to low-quality data, and diverse questions for achieving scaling gains. To overcome the lack of access to training data, the authors create the OpenMathInstruct-2 dataset, consisting of 14 million question-solution pairs, making it nearly eight times larger than the previous largest open-source math reasoning dataset. Finetuning a model using this dataset outperforms previous models on the MATH benchmark by an absolute 15.9%. The paper releases the code, finetuned models, and OpenMathInstruct-2 dataset under a commercially permissive license to accelerate open-source efforts. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make it easier for computers to understand math problems. Right now, most of this progress is locked away because the people who made the computer programs won’t share the data they used. The researchers in this paper did some experiments to figure out how to make better math-solving computers. They found that using clear and simple solutions is important, and that even if the math problems are a bit messy, the computer can still learn from them. They also discovered that having many different types of math questions helps the computer get better at solving them. To help others build on this work, the researchers created a big dataset of math problems and answers, and they’re sharing it with everyone so that anyone can use it to make their own math-solving computers. |
Keywords
» Artificial intelligence » Student model
