تقرير
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
| العنوان: | DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning |
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| المؤلفون: | Wang, Yejie, He, Keqing, Dong, Guanting, Wang, Pei, Zeng, Weihao, Diao, Muxi, Mou, Yutao, Zhang, Mengdi, Wang, Jingang, Cai, Xunliang, Xu, Weiran |
| سنة النشر: | 2024 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Computer Science - Computation and Language, Computer Science - Artificial Intelligence |
| الوصف: | Code Large Language Models (Code LLMs) have demonstrated outstanding performance in code-related tasks. Several instruction tuning 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 distinct reasoning paths increases the code capability of LLMs. (2) Improving one's ability to evaluate the correctness of code solutions also enhances their ability to create it. Comment: 14 pages, 6 figures |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/2402.09136 |
| رقم الأكسشن: | edsarx.2402.09136 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |