Summary of Orca-math: Unlocking the Potential Of Slms in Grade School Math, by Arindam Mitra et al.
Orca-Math: Unlocking the potential of SLMs in Grade School Math
by Arindam Mitra, Hamed Khanpour, Corby Rosset, Ahmed Awadallah
First submitted to arxiv on: 16 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 study challenges the notion that small language models require massive sizes to achieve high accuracy on word problem-solving tasks. By leveraging ensembling techniques and verifier models, researchers demonstrate that smaller models can still perform remarkably well. Specifically, the study hypothesizes that a 34 billion parameter model is sufficient to reach over 80% accuracy on the GSM8K benchmark. To achieve this, models are trained to generate Python code or utilize tools to avoid calculation errors. The findings suggest that ensembling provides a significant boost in accuracy but at the cost of increased computational expense. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Small language models have big goals! They want to solve math problems, but it’s hard. One way they do this is by using many tiny calculations and combining them. This “ensemble” helps get the right answer. But it takes a lot of computer power to do all those calculations. A new study thinks that even smaller models can be super smart if we help them with special tools or let them generate code on their own. |
