Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive
  Learning for Code Generation
With the rise of powerful closed-sourced LLMs (ChatGPT, GPT-4), there are
increasing interests in distilling the capabilies of close-sourced LLMs to
smaller open-sourced LLMs. Previous distillation methods usually prompt ChatGPT
to generate a set of instructions and answers, for the student model to learn.
However, such standard distillation approach neglects the merits and conditions
of the student model. Inspired by modern teaching principles, we design a
personalised distillation process, in which the student attempts to solve a
task first, then the teacher provides an adaptive refinement for the student to
improve. Instead of feeding the student with teacher's prior, personalised
distillation enables personalised learning for the student model, as it only
learns on examples it makes mistakes upon and learns to improve its own
solution. On code generation, personalised distillation consistently
outperforms standard distillation with only one third of the data. With only
2.5-3K personalised examples that incur a data-collection cost of 4-6$, we
boost CodeGen-mono-16B by 7% to achieve 36.4% pass@1 and StarCoder by 12.2% to
achieve 45.8% pass@1 on HumanEval.
65408756e67275697477439f
Hailin Chen
admin_assigned
2023-10-31T08:42:07.283Z
646c440036505117e22f5b76
/avatars/a833947c9d452d673d2598eea7363a73.svg
Hailin Chen
user
hailche
65408756e6727569747743a0
Amrita Saha
admin_assigned
2023-10-31T08:42:16.521Z
6461c2905dba83471db3be53
/avatars/6e36cf86201d590ac729a75d4a439cde.svg
Amrita Saha
user
amritasaha87
65408756e6727569747743a1
Steven Hoi
65408756e6727569747743a2
Shafiq Joty
Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive
  Learning for Code Generation
With the rise of powerful closed-sourced LLMs (ChatGPT, GPT-4), there are
increasing interests in distilling the capabilies of close-sourced LLMs to
smaller open-sourced LLMs. Previous distillation methods usually prompt ChatGPT
to generate a set of instructions and answers, for the student model to learn.
However, such standard distillation approach neglects the merits and conditions
of the student model. Inspired by modern teaching principles, we design a
personalised distillation process, in which the student attempts to solve a
task first, then the teacher provides an adaptive refinement for the student to
improve. Instead of feeding the student with teacher's prior, personalised
distillation enables personalised learning for the student model, as it only
learns on examples it makes mistakes upon and learns to improve its own
solution. On code generation, personalised distillation consistently
outperforms standard distillation with only one third of the data. With only
2.5-3K personalised examples that incur a data-collection cost of 4-6$, we
boost CodeGen-mono-16B by 7% to achieve 36.4% pass@1 and StarCoder by 12.2% to
achieve 45.8% pass@1 on HumanEval.
A personalized distillation process improves smaller open-sourced LLMs by focusing on mistakes made by the student model, outperforming standard distillation methods with limited data.
LLMs ChatGPT GPT-4 distillation methods student model teacher model adaptive refinement code generation HumanEval CodeGen-mono-16B StarCoder