WizardCoder: Empowering Code Large Language Models with Evol-Instruct (AI summary)

Key Points

1. The paper introduces "WizardCoder," which enhances the performance of open-source Code Large Language Model (LLM), StarCoder, through the Code Evol-Instruct method tailored for code domain tasks.

2. The experimental results demonstrate that WizardCoder outperforms all existing open-source Code LLMs and even surpasses closed LLMs, such as Anthropic’s Claude and Google’s Bard, on four prominent code generation benchmarks: HumanEval, HumanEval+, MBPP, and DS-1000.

3. The study highlights the significance of fine-grained instruction tuning in the code domain, building upon the evolution of existing instruction data to generate complex and diverse datasets using the Evol-Instruct method.

4. The paper presents a detailed methodology for training WizardCoder and evolving the code instruction-following training set through Evol-Instruct.

5. WizardCoder's superior performance on benchmarks is showcased through pass@1 scores, surpassing both closed-source and open-source Code LLMs, signifying its state-of-the-art performance in code generation tasks.

6. The study compares WizardCoder with various closed-source and open-source models, demonstrating the model's substantial performance advantage in code generation tasks.

7. WizardCoder's performance is highlighted through a comprehensive comparison on HumanEval, HumanEval+, MBPP, and DS-1000 benchmarks, showcasing its superior performance in data science problems across various libraries.

8. The paper includes an ablation study on the number of data evolution rounds, indicating the effectiveness of the approach by selecting the data evolved during the third round as the ultimate dataset.

9. The study concludes with implications for future work on further enhancing the performance of WizardCoder and considering the ethical and societal impacts of large language models in code generation.

Summary

The research paper introduces "WizardCoder," an enhanced Code Large Language Model (LLM) that employs the Code Evol-Instruct method for intricate code instruction fine-tuning. The study describes modifications to the evolutionary prompt process tailored for code-related tasks, the fine-tuning of the Code LLM using the evolved code instruction-following training set, and the achievement of superior performance over existing open-source Code LLMs.

Experimental results from four code generation benchmarks demonstrate that the "WizardCoder" outperforms open-source Code LLMs, achieving substantial improvement in pass@1 scores. The paper compares "WizardCoder" to closed-source LLMs and other open-source models, highlighting its superior performance. Additional information is provided about the evolutionary prompt process, training procedures, and the performance of "WizardCoder" on specific benchmarks like HumanEval, HumanEval+, MBPP, and DS-1000.

The paper concludes with implications for future work and considers the broader impact of the research. The referenced study also offers detailed information on other relevant research and models in the field of large language models, code generation, and instruction fine-tuning.

Reference: https://arxiv.org/abs/2306.08568