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Automating code review with large language models

Paper Authors:

Junyi Lu,

Lei Yu,

Xiaojia Li,

Li Yang,

Chun Zuo


Key Details

LLaMA-Reviewer matches specialized models' performance using just a small LLaMA model

It uses parameter-efficient tuning for computational efficiency

Performance is strong in review comment generation and necessity prediction

Input format impacts results - raw code works better

Instruction tuning can enhance model understanding

AI generated summary

Automating code review with large language models

This paper proposes LLaMA-Reviewer, a framework that leverages large language models (LLMs) like LLaMA for automating code review tasks. It uses parameter-efficient fine-tuning methods to reduce computational demands while achieving performance on par with specialized models. The approach is evaluated on public datasets and demonstrates LLMs' potential despite using the smallest LLaMA variant.

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