Summary of Mentor-kd: Making Small Language Models Better Multi-step Reasoners, by Hojae Lee et al.
Mentor-KD: Making Small Language Models Better Multi-step Reasoners
by Hojae Lee, Junho Kim, SangKeun Lee
First submitted to arxiv on: 11 Oct 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 This research proposes a novel approach called Mentor-KD that leverages Large Language Models (LLMs) to transfer their multi-step reasoning capabilities to smaller language models, addressing two major challenges: insufficient data quality and soft label provision. The proposed method uses an intermediate-sized task-specific fine-tuned model as a mentor to provide additional Chain-of-Thought (CoT) annotations and soft labels for the student model during Knowledge Distillation (KD). This approach is shown to be effective across various models and complex reasoning tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Mentor-KD helps smaller language models learn from large language models. It fixes two problems that others didn’t solve: bad data quality and not having good answers to work with. To do this, it uses a special model as a teacher to give extra help and guidance during training. This approach works well for different types of models and tricky thinking tasks. |
Keywords
» Artificial intelligence » Knowledge distillation » Student model
