content.raw: All the way from Korea, a novel approach called Mentor-KD significantly improves the reasoning abilities of small language models.
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content.value: All the way from Korea, a novel approach called Mentor-KD significantly improves the reasoning abilities of small language models.
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content.raw: 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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content.value: 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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content.raw: Broadly, it’s a two-stage process:
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content.value: Broadly, it’s a two-stage process:
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content.raw: 1) Fine-tune the mentor on filtered Chain-of-Thought (CoT) annotations from an LLM teacher.  
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content.value: 1) Fine-tune the mentor on filtered Chain-of-Thought (CoT) annotations from an LLM teacher.  
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content.raw: 2) Use the mentor to generate additional CoT rationales and soft probability distributions.
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content.value: 2) Use the mentor to generate additional CoT rationales and soft probability distributions.
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content.raw: The student model is then trained using:
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content.value: The student model is then trained using:
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content.raw: - CoT rationales from both the teacher and mentor (rationale distillation).  
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content.value: - CoT rationales from both the teacher and mentor (rationale distillation).  
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content.raw: - Soft labels from the mentor (soft label distillation).
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content.value: - Soft labels from the mentor (soft label distillation).
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content.raw: Results show that Mentor-KD consistently outperforms baselines, with up to 5% accuracy gains on some tasks.
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content.value: Results show that Mentor-KD consistently outperforms baselines, with up to 5% accuracy gains on some tasks.
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content.raw: 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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content.value: 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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content.raw: This work opens up exciting possibilities for making smaller, more efficient language models better at complex reasoning tasks.
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content.value: This work opens up exciting possibilities for making smaller, more efficient language models better at complex reasoning tasks.
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content.raw: What are your thoughts on this approach?
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content.value: What are your thoughts on this approach?
rawContent: 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?
reactions.reaction: 👀
reactions.users: John6666