Summary of Siked: Self-guided Iterative Knowledge Distillation For Mathematical Reasoning, by Shivam Adarsh et al.
SIKeD: Self-guided Iterative Knowledge Distillation for mathematical reasoning
by Shivam Adarsh, Kumar Shridhar, Caglar Gulcehre, Nicholas Monath, Mrinmaya Sachan
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 SIKeD method leverages Large Language Models (LLMs) to transfer their reasoning skills to smaller models, enabling them to solve multistep reasoning tasks. Unlike traditional distillation methods, SIKeD allows smaller models to learn which strategy is suitable for a given task while continuously learning to solve the task using different strategies. The method iteratively trains the smaller model to generate on-policy outputs and combines these with LLM data, allowing it to prioritize the most effective strategy. Experimental results show that SIKeD outperforms traditional distillation techniques across various mathematical reasoning datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes a new way to teach smaller models how to solve problems by showing them different strategies for solving math problems. This is done by using larger language models as teachers, which can demonstrate many ways of solving the same problem. The method allows the smaller model to learn which strategy is best for each specific problem and then use that strategy to solve it. The results show that this new method works better than traditional methods on a variety of math problems. |
Keywords
» Artificial intelligence » Distillation
