Summary of Distilling Mathematical Reasoning Capabilities Into Small Language Models, by Xunyu Zhu et al.
Distilling Mathematical Reasoning Capabilities into Small Language Models
by Xunyu Zhu, Jian Li, Yong Liu, Can Ma, Weiping Wang
First submitted to arxiv on: 22 Jan 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 abstract presents a novel approach to compressing advanced Large Language Models (LLMs) into smaller Small Language Models (SLMs) without sacrificing performance. The authors introduce Equation-of-Thought Distillation (EoTD), a technique that encapsulates mathematical reasoning capabilities into equation-based representations, and develop an EoTD dataset for fine-tuning SLMs. Additionally, they propose the Ensemble Thoughts Distillation (ETD) framework to enhance the reasoning performance of SLMs. The authors demonstrate the effectiveness of their approach through experimental results, showcasing significant boosts in reasoning abilities for SLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps make advanced language models more accessible by shrinking them down without losing their smart thinking skills. Researchers created a new way to squeeze complex math problems into smaller equations, allowing smaller language models to understand and solve these problems just as well as bigger ones. This breakthrough could lead to more people using AI-powered language tools in the future. |
Keywords
» Artificial intelligence » Distillation » Fine tuning
