Researchers from Nanyang Technological University and Salesforce Research have introduced personalized distillation for code generation tasks. The method involves a student model attempting a task and receiving adaptive refinement from a teacher model, outperforming standard distillation methods with only one-third of the data. Personalized distillation improves the performance of open-source pretrained models in code generation tasks. The approach offers a solution to distill the capabilities of closed-source large language models into smaller open-source models. The study suggests further investigation into dynamic data collection during fine-tuning and extending personalized distillation to other domains.
This AI Paper Introduces a Novel Personalized Distillation Process: Enhancing Open-Source LLMs with Adaptive Learning from Closed-Source Counterparts
Researchers from Nanyang Technological University, Singapore, and Salesforce Research have developed a personalized distillation process for code generation tasks. This approach combines a student model’s initial attempt with adaptive refinement from a teacher model, resulting in superior results with only a third of the data. The personalized distillation method has been tested on two code generation models, CodeGen-mono-16B and StarCoder, and has shown substantial performance improvements in HumanEval assessments.
Key Highlights:
– Personalized distillation consistently outperforms standard methods, achieving better results with only one-third of the data.
– The approach enhances the performance of open-source pretrained models, such as CodeGen-mono-16B and StarCoder, in code generation tasks.
– It addresses the limitations of closed-source large language models (LLMs) like ChatGPT and GPT-4 in terms of availability, cost, ethics, and data privacy concerns.
– Personalized distillation offers a solution to distill the capabilities of closed-source LLMs into smaller open-source LLMs.
The study compared personalized distillation (PERsD) with standard distillation (STAND) and input-personalized distillation (INPD). PERsD consistently outperformed the other methods in code generation tasks, achieving significant improvements with only one-third of the data. Multi-step inference enhanced the quality of answers in PERsD-refine and PERsD-combine models, showcasing their ability to refine solutions based on execution error feedback.
PERsD introduced a method for customizing labeled data to student model capacity, yielding more effective learning. It outperformed standard distillation in code generation on HumanEval and MBPP datasets, benefiting from higher data quality, multi-round distillation, and self-rectification via execution feedback. The approach represents a promising advancement in distilling closed-source LLM capabilities into open-source models.
Practical Solutions and Value:
– Investigate online personalized distillation to collect data dynamically during fine-tuning, potentially enhancing student models.
– Explore scalable methods for personalized distillation that don’t rely on human annotation, addressing limitations like the impact of mixing personalized and non-personalized labels.
– Extend personalized distillation to other domains to assess its effectiveness.
– Consider using personalized distillation for distilling closed-source LLM capabilities into open-source models, advancing model distillation further.
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