phi2-gsm8k-rephrase-high-confidence-training

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This is the result of 3 iterations of self improvement of the model on a subset of GSM8K problems where the base Phi-2 was less confident. We utilized self consistency evaluation along with execution traces to self-select high quality self-generated samples for training without looking at the ground truth answers. This improved the base model Phi-2 accuracy by about 6% on GSM8K dataset - both the test set and the harder to solve subset of the training data.

• Developed by: Stanford University team: Artyom Shaposhnikov, Roberto Garcia, Shubhra Mishra
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• Language(s) (NLP): Python
• License: MIT
• Finetuned from model [optional]: microsoft/phi-2

Model Sources [optional]

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• Repository: https://github.com/deepbrain/CS224N
• Paper [optional]: "Self-Improvement for Math Problem-Solving in Small Language Models"

Uses

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Training Details

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Training Hyperparameters

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Testing Data, Factors & Metrics

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Summary

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Environmental Impact

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Carbon emissions can be estimated using the Machine Learning Impact calculator presented in Lacoste et al. (2019).

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Technical Specifications [optional]

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