DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
Yejie Wang, Keqing He, Guanting Dong, Pei Wang, Weihao Zeng, Muxi Diao, Weiran Xu, Jingang Wang
Abstract
Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Various instruction finetuning approaches have been proposed to boost the code generation performance of pre-trained Code LLMs. In this paper, we introduce a diverse instruction model DolphCoder with self-evaluating for code generation. It learns diverse instruction targets and combines a code evaluation objective to enhance its code generation ability. Our model achieves superior performance on the HumanEval and MBPP benchmarks, demonstrating new insights for future code instruction tuning work. Our key findings are: (1) Augmenting more diverse responses with more distinct reasoning paths increases the code capability of LLMs. (2) Improving one’s ability to evaluate the correctness of code also enhances their ability to create it.
BibTeX
@inproceedings{wang-etal-2024-dolphcoder,
title = "{D}olph{C}oder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning",
author = "Wang, Yejie and
He, Keqing and
Dong, Guanting and
Wang, Pei and
Zeng, Weihao and
Diao, Muxi and
Xu, Weiran and
Wang, Jingang and
Zhang, Mengdi and
Cai, Xunliang",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = aug,
year = "2024",
address = "Bangkok, Thailand",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.acl-long.259/",
doi = "10.18653/v1/2024.acl-long.259",
pages = "4706--4721"
}