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WaveCoder: Widespread And Versatile Enhancement For Code Large Language Models By Instruction Tuning

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arxiv 2312.14187 v5 pith:W4WXONHP submitted 2023-12-20 cs.CL cs.AIcs.SE

Zhaojian Yu , Xin Zhang , Ning Shang , Yangyu Huang , Can Xu , Yishujie Zhao , Wenxiang Hu , Qiufeng Yin This is my paper
classification cs.CLcs.AIcs.SE
keywords codecode-relatedinstructiontasksmodelsgeneralizationllmstuning
verification ladder T0 review T1 audit T2 compute T3 formal
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Recent work demonstrates that, after instruction tuning, Code Large Language Models (Code LLMs) can obtain impressive capabilities to address a wide range of code-related tasks. However, current instruction tuning methods for Code LLMs mainly focus on the traditional code generation task, resulting in poor performance in complex multi-task scenarios. In this paper, we concentrate on multiple code-related tasks and present WaveCoder, a series of Code LLMs trained with Widespread And Versatile Enhanced instruction data. To enable the models to tackle complex code-related tasks, we propose a method to stably generate diverse, high-quality instruction data from open source code dataset in multi-task scenarios and obtain CodeSeaXDataset, a dataset comprising 19,915 instruction instances across 4 code-related tasks, which is aimed at improving the generalization ability of Code LLM. Our experiments demonstrate that WaveCoder models significantly outperform other open-source models in terms of the generalization ability across different code-related tasks. Moreover, WaveCoder-Ultra-6.7B presents the state-of-the-art generalization abilities on a wide range of code-related tasks.

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Cited by 5 Pith papers

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  5. Instruction Learning Paradigms: A Dual Perspective on White-box and Black-box LLMs

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    A hybrid black-box and white-box instruction optimizer, built on InstructZero and INSTINCT, reports the highest mean score on 30 tasks but with small margins, missing error bars, and unreleased code.

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