FranxYao/FlanT5-CoT-Specialization
Implementation of ICML 23 Paper: Specializing Smaller Language Models towards Multi-Step Reasoning.
This project helps machine learning engineers and researchers train smaller language models to perform complex, multi-step reasoning tasks more effectively. It takes pre-processed datasets that include 'chain-of-thought' examples and outputs a specialized, smaller language model capable of solving problems that require intermediate reasoning steps. The primary users are those focused on optimizing language model performance for intricate problem-solving.
132 stars. No commits in the last 6 months.
Use this if you are a machine learning engineer or researcher looking to specialize a smaller language model for multi-step reasoning tasks, aiming for improved performance without needing a massive model.
Not ideal if you are looking for a pre-trained, ready-to-use model for general natural language tasks without specific multi-step reasoning requirements.
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132
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Jupyter Notebook
License
MIT
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Last pushed
Jun 18, 2023
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