WaveCoder: Widespread And Versatile Enhanced Instruction Tuning with
  Refined Data Generation
Authors: name: Zhaojian Yu, name: Xin Zhang, name: Ning Shang, name: Yangyu Huang, name: Can Xu, name: Yishujie Zhao, name: Wenxiang Hu, name: Qiufeng Yin

Recent work demonstrates that, after being fine-tuned on a high-quality
instruction dataset, the resulting model can obtain impressive capabilities to
address a wide range of tasks. However, existing methods for instruction data
generation often produce duplicate data and are not controllable enough on data
quality. In this paper, we extend the generalization of instruction tuning by
classifying the instruction data to 4 code-related tasks and propose a
LLM-based Generator-Discriminator data process framework to generate diverse,
high-quality instruction data from open source code. Hence, we introduce
CodeOcean, a dataset comprising 20,000 instruction instances across 4 universal
code-related tasks,which is aimed at augmenting the effectiveness of
instruction tuning and improving the generalization ability of fine-tuned
model. Subsequently, we present WaveCoder, a fine-tuned Code LLM with
Widespread And Versatile Enhanced instruction tuning. This model is
specifically designed for enhancing instruction tuning of Code Language Models
(LLMs). Our experiments demonstrate that Wavecoder models outperform other
open-source models in terms of generalization ability across different
code-related tasks at the same level of fine-tuning scale. Moreover, Wavecoder
exhibits high efficiency in previous code generation tasks. This paper thus
offers a significant contribution to the field of instruction data generation
and fine-tuning models, providing new insights and tools for enhancing
performance in code-related tasks.