Post
391
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?
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?