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All the way from Korea, a novel approach called Mentor-KD significantly improves the reasoning abilities of small language models.
All the way from Korea, a novel approach called Mentor-KD significantly improves the reasoning abilities of small language models.
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Mentor-KD introduces an intermediate-sized "mentor" model to augment training data and provide soft labels during knowledge distillation from large language models (LLMs) to smaller models.
Mentor-KD introduces an intermediate-sized "mentor" model to augment training data and provide soft labels during knowledge distillation from large language models (LLMs) to smaller models.
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Broadly, it’s a two-stage process:
Broadly, it’s a two-stage process:
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1) Fine-tune the mentor on filtered Chain-of-Thought (CoT) annotations from an LLM teacher.  
1) Fine-tune the mentor on filtered Chain-of-Thought (CoT) annotations from an LLM teacher.  
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2) Use the mentor to generate additional CoT rationales and soft probability distributions.
2) Use the mentor to generate additional CoT rationales and soft probability distributions.
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The student model is then trained using:
The student model is then trained using:
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- CoT rationales from both the teacher and mentor (rationale distillation).  
- CoT rationales from both the teacher and mentor (rationale distillation).  
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- Soft labels from the mentor (soft label distillation).
- Soft labels from the mentor (soft label distillation).
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Results show that Mentor-KD consistently outperforms baselines, with up to 5% accuracy gains on some tasks.
Results show that Mentor-KD consistently outperforms baselines, with up to 5% accuracy gains on some tasks.
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Mentor-KD is especially effective in low-resource scenarios, achieving comparable performance to baselines while using only 40% of the original training data.
Mentor-KD is especially effective in low-resource scenarios, achieving comparable performance to baselines while using only 40% of the original training data.
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This work opens up exciting possibilities for making smaller, more efficient language models better at complex reasoning tasks.
This work opens up exciting possibilities for making smaller, more efficient language models better at complex reasoning tasks.
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What are your thoughts on this approach?
What are your thoughts on this approach?
All the way from Korea, a novel approach called Mentor-KD significantly improves the reasoning abilities of small language models.

Mentor-KD introduces an intermediate-sized "mentor" model to augment training data and provide soft labels during knowledge distillation from large language models (LLMs) to smaller models.

Broadly, it’s a two-stage process:
1) Fine-tune the mentor on filtered Chain-of-Thought (CoT) annotations from an LLM teacher.  
2) Use the mentor to generate additional CoT rationales and soft probability distributions.

The student model is then trained using:
- CoT rationales from both the teacher and mentor (rationale distillation).  
- Soft labels from the mentor (soft label distillation).

Results show that Mentor-KD consistently outperforms baselines, with up to 5% accuracy gains on some tasks.

Mentor-KD is especially effective in low-resource scenarios, achieving comparable performance to baselines while using only 40% of the original training data.

This work opens up exciting possibilities for making smaller, more efficient language models better at complex reasoning tasks.

What are your thoughts on this approach?
👀
John6666