Enhancing Code Generation Performance of Smaller Models through LLM Reasoning Distillation

핵심 개념

Enhancing code generation performance of smaller models by distilling the reasoning ability of LLMs through the CodePLAN framework.

초록

Abstract:

• Large Language Models (LLMs) have advanced code generation using 'Chain-of-Thought' prompting.
• Smaller models struggle with reasoning, affecting code generation.
• CodePLAN transfers LLMs' reasoning to smaller models through distillation.
• Multi-task learning enhances code generation and solution plan tasks.

Introduction:

• Automatic code generation history dates back decades.
• Neural network techniques like LLMs improve intelligent code generation.
• Smaller models lag in reasoning compared to LLMs, impacting complex programming tasks.
• In-context learning (ICL) of LLMs is exceptional in crafting solution plans.

Data Extraction:

• "our approach improves the smaller model’s code generation performance (measured in pass@1 metric) by over 130% on the challenging APPS benchmark."

Quotations:

• "CodePLAN utilizes multi-task learning to imbue smaller models with LLMs’ reasoning capabilities."
• "Our experiments show that in comparison to the conventional fine-tuning approach, our approach improves the smaller model’s code generation performance by over 130% on the challenging APPS benchmark."

통계

"Our approach improves the smaller model’s code generation performance (measured in pass@1 metric) by over 130% on the challenging APPS benchmark."

인용구

"CodePLAN utilizes multi-task learning to imbue smaller models with LLMs’ reasoning capabilities."
"Our experiments show that in comparison to the conventional fine-tuning approach, our approach improves the smaller model’s code generation performance by over 130% on the challenging APPS benchmark."

핵심 통찰 요약

Enhancing Code Generation Performance of Smaller Models by Distilling the Reasoning Ability of LLMs

by Zhihong Sun,... 게시일 arxiv.org 03-21-2024

https://arxiv.org/pdf/2403.13271.pdf

더 깊은 질문

How does solution plan quality impact model performance in code generation?

The quality of solution plans has a significant impact on the performance of models in code generation. High-quality solution plans can greatly boost the model's ability to generate accurate and functional code. When models are guided by precise and well-thought-out solution plans, they are more likely to produce correct codes that pass unit tests efficiently. On the other hand, subpar or inaccurate solution plans may lead the model astray, resulting in lower accuracy and potentially degrading its overall performance. Therefore, ensuring high-quality solution plans is crucial for enhancing the effectiveness of models in generating accurate and reliable code.

What distinguishes our approach from conventional code post-processing methods in code generation?

Our approach differs from conventional code post-processing methods by actively focusing on improving the model's inherent capabilities rather than relying solely on ranking or filtering generated codes after the fact. While traditional post-processing techniques may involve ranking potential codes based on certain criteria, they do not inherently enhance the model's ability to generate correct code from scratch. In contrast, our approach encourages models to produce more accurate codes directly by guiding them with high-quality solution plans during both training and inference stages. By emphasizing the generation of precise solutions upfront, we aim to optimize and refine the entire process of code generation within the model itself.

How can CodePLAN be adapted across different programming languages and coding scenarios?

CodePLAN can be adapted across different programming languages and coding scenarios through careful consideration of language-specific syntax rules, semantics, and problem-solving strategies unique to each language. To adapt CodePLAN effectively:

Language-specific Training: Train CodePLAN using datasets specific to different programming languages to capture language nuances.
Syntax Handling: Develop mechanisms within CodePLAN to handle variations in syntax rules among languages.
Semantic Understanding: Incorporate semantic understanding modules tailored for each language into CodePLAN.
Problem Domain Adaptation: Fine-tune CodePLAN on diverse coding scenarios representing various problem domains.
Cross-Language Transfer Learning: Explore transfer learning techniques to leverage knowledge gained from one language when adapting it to another.

By incorporating these adaptations into CodePLAN's framework design and training methodology, it can effectively extend its capabilities across a wide range of programming languages and coding scenarios while maintaining optimal performance levels for each specific context.