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％。

个性化蒸馏：为代码生成赋能开源 LLMs 的自适应学习

Personalised Distillation: Empowering Open-Sourced LLMs with Adaptive  Learning for Code Generation

The structure of curriculum plays a vital role in our learning process, both
as children and adults. Presenting material in ascending order of difficulty
that also exploits prior knowledge can have a significant impact on the rate of
learning. However, the notion of difficulty and prior knowledge differs from
person to person. Motivated by the need for a personalised curriculum, we
present a novel method of curriculum learning for vocabulary words in the form
of visual prompts. We employ a reinforcement learning model grounded in
pedagogical theories that emulates the actions of a tutor. We simulate three
students with different levels of vocabulary knowledge in order to evaluate the
how well our model adapts to the environment. The results of the simulation
reveal that through interaction, the model is able to identify the areas of
weakness, as well as push students to the edge of their ZPD. We hypothesise
that these methods can also be effective in training agents to learn language
representations in a simulated environment where it has previously been shown
that order of words and prior knowledge play an important role in the efficacy
of language learning.

通过视觉提示将词汇学习个性化，通过强化学习建模评估其适应性，并为代理人的语言学习提供训练方法。