Code Comparison Tuning for Code Large Language Models
Yufan Jiang Qiaozhi He Xiaomin Zhuang Zhihua Wu
We present Code Comparison Tuning (CCT), a simple and effective tuning method
for code large language models (Code LLMs) to better handle subtle code errors.
Specifically, we integrate the concept of comparison into instruction tuning,
both at the token and sequence levels, enabling the model to discern even the
slightest deviations in code. To compare the original code with an erroneous
version containing manually added code errors, we use token-level preference
loss for detailed token-level comparisons. Additionally, we combine code
segments to create a new instruction tuning sample for sequence-level
comparisons, enhancing the model's bug-fixing capability. Experimental results
on the HumanEvalFix benchmark show that CCT surpasses instruction tuning in
pass@1 scores by up to 4 points across diverse code LLMs, and extensive
analysis demonstrates the effectiveness of our method.