All the way from Korea, a novel approach called Mentor-KD significantly improves the reasoning abilities of small language models.
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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.
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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.
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Broadly, it’s a two-stage process:
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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.  
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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.
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2) Use the mentor to generate additional CoT rationales and soft probability distributions.
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The student model is then trained using:
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The student model is then trained using:
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- CoT rationales from both the teacher and mentor (rationale distillation).  
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- CoT rationales from both the teacher and mentor (rationale distillation).  
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- Soft labels from the mentor (soft label distillation).
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- 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.
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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.
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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.
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This work opens up exciting possibilities for making smaller, more efficient language models better at complex reasoning tasks.
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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?
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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