How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with
  High-Quality Data
Authors: name: Yejie Wang, name: Keqing He, name: Dayuan Fu, name: Zhuoma Gongque, name: Heyang Xu, name: Yanxu Chen, name: Zhexu Wang, name: Yujia Fu, name: Guanting Dong, name: Muxi Diao, name: Jingang Wang, name: Mengdi Zhang, name: Xunliang Cai, name: Weiran Xu

Recently, there has been a growing interest in studying how to construct
better code instruction tuning data. However, we observe Code models trained
with these datasets exhibit high performance on HumanEval but perform worse on
other benchmarks such as LiveCodeBench. Upon further investigation, we find
that many datasets suffer from severe data leakage. After cleaning up most of
the leaked data, some well-known high-quality datasets perform poorly. This
discovery reveals a new challenge: identifying which dataset genuinely qualify
as high-quality code instruction data. To address this, we propose an efficient
code data pruning strategy for selecting good samples. Our approach is based on
three dimensions: instruction complexity, response quality, and instruction
diversity. Based on our selected data, we present XCoder, a family of models
finetuned from LLaMA3. Our experiments show XCoder achieves new
state-of-the-art performance using fewer training data, which verify the
effectiveness of our data strategy. Moreover, we perform a comprehensive
analysis on the data composition and find existing code datasets have different
characteristics according to their construction methods, which provide new
insights for future code LLMs. Our models and dataset are released in
https://github.com/banksy23/XCoder