Summary of Mind: Math Informed Synthetic Dialogues For Pretraining Llms, by Syeda Nahida Akter et al.
MIND: Math Informed syNthetic Dialogues for Pretraining LLMs
by Syeda Nahida Akter, Shrimai Prabhumoye, John Kamalu, Sanjeev Satheesh, Eric Nyberg, Mostofa Patwary, Mohammad Shoeybi, Bryan Catanzaro
First submitted to arxiv on: 15 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computation and Language (cs.CL)
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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 proposed Math Informed syNthetic Dialogue (MIND) generation method improves the mathematical reasoning ability of large language models (LLMs). By generating synthetic conversations based on OpenWebMath (OWM), a new math corpus, MIND-OWM, is created. Experiments reveal that incorporating knowledge gaps between dialog participants is essential for generating high-quality math data. The study also identifies an effective way to format and integrate synthetic and raw data during pretraining, emphasizing the need to restructure raw data rather than use it as-is. Compared to pretraining just on raw data, a model pretrained on MIND-OWM shows significant boosts in mathematical reasoning and specialized knowledge tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computer-generated conversations to make large language models better at math problems. They create new training data by talking about math concepts with each other, which helps the models learn to solve harder math problems. The researchers found that these conversations are most helpful when they include gaps in understanding between the two “people” having a conversation. By using this approach, they can improve the language model’s ability to understand and solve complex math problems. |
Keywords
» Artificial intelligence » Language model » Pretraining
