Summary of Dolphcoder: Echo-locating Code Large Language Models with Diverse and Multi-objective Instruction Tuning, by Yejie Wang et al.
DolphCoder: Echo-Locating Code Large Language Models with Diverse and Multi-Objective Instruction Tuning
by Yejie Wang, Keqing He, Guanting Dong, Pei Wang, Weihao Zeng, Muxi Diao, Yutao Mou, Mengdi Zhang, Jingang Wang, Xunliang Cai, Weiran Xu
First submitted to arxiv on: 14 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper introduces a novel approach to improving the performance of pre-trained Code Large Language Models (Code LLMs) in generating code for various programming tasks. The authors propose a diverse instruction model called DolphCoder, which learns to generate multiple instruction targets and combines them with a code evaluation objective to enhance its code generation capabilities. The proposed model outperforms existing approaches on two benchmark datasets, HumanEval and MBPP, demonstrating new insights for future research in this area. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about making computers better at writing code. It’s like teaching a super smart language model how to write code that humans would use. Right now, these models are really good at answering questions, but not so great at creating code on their own. The authors of the paper came up with a new way to make these models better by giving them more instructions and helping them learn how to evaluate whether the code they generate is correct or not. This makes the models even better at generating code that humans can use. |
Keywords
* Artificial intelligence * Language model
