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cs.SE2025
Fine-Tuning LLMs to Analyze Multiple Dimensions of Code Review: A Maximum Entropy Regulated Long Chain-of-Thought Approach
Yongda Yu, Guohao Shi, Xianwei Wu +8
Large Language Models (LLMs) have shown great potential in supporting automated code review due to their impressive capabilities in context understanding and reasoning. However, th…
cs.SE2025
SynthCoder: A Synthetical Strategy to Tune LLMs for Code Completion
Dongjun Yu, Xiao Yan, Zhenrui Li +6
Code completion is a prominent application of Large Language Models (LLMs) in software engineering. Due to the near real-time response requirements of this task, base models with s…
cs.SE2025
Distilling Desired Comments for Enhanced Code Review with Large Language Models
Yongda Yu, Lei Zhang, Guoping Rong +9
There has been a growing interest in using Large Language Models (LLMs) for code review thanks to their proven proficiency in code comprehension. The primary objective of most revi…