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.

通过个性化蒸馏的方式，我们在代码生成方面取得了显著的进展，仅使用 1/3 的数据就能提升性能，并且通过 2.5-3K 的个性化示例，带来 4-6 美元的数据收集成本，使 CodeGen-mono-16B 模型 pass@1 达到 36.4％，StarCoder 模型 pass@1 达到 45.8％。