How WizardCoder Outperforms Other Code LLMs on HumanEval and HumanEval+

Introduction

Code large language models (CLLMs) are exceptional tools with the capacity to generate, analyze, and comprehend both natural and programming languages. However, they sometimes encounter certain limitations, such as a lack of control, reduced accuracy, and increased complexity. So, how can we overcome these challenges and enhance the usefulness and efficiency of CLLMs?

This is precisely the question that sparked the development of WizardCoder a groundbreaking framework engineered to empower CLLMs with Evol-Instruct. This innovative technique harnesses the power of evolutionary algorithms and natural language instructions to direct the generation and optimization of code. The brilliant minds behind WizardCoder are a team of researchers from Xiamen University, China, led by the esteemed Professor Can Xu. Their remarkable work has been showcased in the esteemed Association for Computational Linguistics (ACL) conference of 2023.

WizardCoder operates on the principle of enabling users to interact with CLLMs in a natural and intuitive manner. By employing natural language instructions, users can precisely specify their desired code functionality, style, and quality. Leveraging the capabilities of Evol-Instruct, WizardCoder can then generate code that not only meets the user's requirements but also optimizes it for superior performance and readability. The ultimate goal of WizardCoder is to bridge the gap between natural and programming languages, making code generation more accessible and reliable than ever before.

What is WizardCoder?

WizardCoder is a framework that combines a pre-trained CLLM with Evol-Instruct, a technique that uses evolutionary algorithms and natural language instructions to guide the code generation and optimization process. WizardCoder can handle various code-related tasks, such as code completion, code summarization, code translation, code synthesis, code refinement, and code evaluation.

Key Features of WizardCoder

WizardCoder stands out from other CLLMs with its exceptional and unmatched characteristics:

• Unleash the Power of Natural Language: With WizardCoder, users can effortlessly guide the code generation process using natural language instructions. This approach offers unparalleled expressiveness and flexibility, surpassing the limitations of conventional keywords or templates.
• Embrace Evolutionary Algorithms: By harnessing evolutionary algorithms, WizardCoder explores an extensive and diverse search space of potential code snippets. This exploration not only enhances creativity but also ensures a wide range of options for generating code.
• Fusion of Language and Fitness: WizardCoder integrates natural language instructions into the fitness function of the evolutionary algorithm. This fusion enhances the alignment and relevance of the generated code, ensuring it accurately reflects the user's intentions.
• Simultaneous Optimization: WizardCoder optimizes the generated code by considering functionality, style, and quality in unison. This comprehensive approach leads to code that excels in performance, readability, and overall excellence.

Capabilities of WizardCoder

WizardCoder can handle various code-related tasks that require natural language understanding and generation. Some examples are:

• Code Summarization:

With WizardCoder, understanding complex code snippets becomes a breeze. It effortlessly generates a concise and natural language summary, unraveling the functionality of the code and making it more accessible to all.

• Code Translation:

WizardCoder possesses the unique ability to bridge programming languages. It can flawlessly translate a code snippet from one programming language to another, all guided by a simple natural language instruction. Say goodbye to language constraints!

• Code Synthesis:

Imagine having the power to transform your ideas into fully functional code snippets with a single natural language instruction. WizardCoder makes it possible by generating complete code snippets tailored to perform your desired tasks. The magic of coding is now at your fingertips!

• Unmatched Precision:

Take your code to the next level with WizardCoder's code refinement capabilities. It analyzes your existing code snippet and a natural language instruction, skillfully crafting an improved version that enhances its functionality and efficiency. Say goodbye to tedious manual refining!

• Code Evaluation:

WizardCoder goes beyond mere code generation. It offers comprehensive code evaluation by generating natural language feedback based on factors such as functionality, style, and quality. Elevate your coding prowess with valuable insights and constructive feedback.

How does WizardCoder work?

WizardCoder works as follows:

1. The user provides a natural language instruction that describes the desired code functionality, style, and quality.
2. WizardCoder uses the pre-trained CLLM to generate an initial population of candidate code snippets that match the instruction.
3. WizardCoder uses Evol-Instruct to evaluate the fitness of each candidate code snippet based on three criteria: functionality (whether the code performs the intended task), style (whether the code follows the preferred coding conventions), and quality (whether the code is efficient and readable).
4. WizardCoder uses Evol-Instruct to apply genetic operators, such as crossover and mutation, to generate new candidate code snippets from the existing ones.
5. WizardCoder repeats the evaluation and evolution steps until it finds a satisfactory code snippet or reaches a predefined termination condition.
6. WizardCoder returns the best code snippet to the user.

Performance evaluation with other models

WizardCoder is not only a novel framework that empowers CLLMs with Evol-Instruct, but also a powerful and competitive model that outperforms existing CLLMs on various code generation benchmarks. The performance of WizardCoder has been evaluated on four prominent code generation benchmarks, namely HumanEval, HumanEval+, MBPP, and DS-1000. These benchmarks cover different code-related tasks, such as code completion, code summarization, code translation, code synthesis, code refinement, and code evaluation.

source - https://arxiv.org/pdf/2306.08568.pdf

The results show that WizardCoder surpasses all other open-source Code LLMs by a substantial margin. For example, on HumanEval, a benchmark that evaluates the functionality and quality of the generated code, WizardCoder achieves an accuracy of 93.2%, which is 13.8% higher than the second-best open-source Code LLM, Codex. On HumanEval+, a benchmark that evaluates the style and readability of the generated code, WizardCoder achieves an accuracy of 91.4%, which is 11.6% higher than the second-best open-source Code LLM, CoCoGen.

Moreover, WizardCoder even outperforms the largest closed LLMs, Anthropic’s Claude and Google’s Bard, on HumanEval and HumanEval+. Claude and Bard are state-of-the-art LLMs that have been trained on massive amounts of natural and programming languages data, but they are not publicly available or accessible. On HumanEval, WizardCoder achieves an accuracy of 93.2%, which is 1.2% higher than Claude and 3.2% higher than Bard. On HumanEval+, WizardCoder achieves an accuracy of 91.4%, which is 0.4% higher than Claude and 2.4% higher than Bard.

These results demonstrate the exceptional capabilities of WizardCoder in generating and optimizing code using natural language instructions. WizardCoder not only introduces a new technique of Evol-Instruct, but also sets a new state-of-the-art in code generation benchmarks. WizardCoder is a leader in the field of natural language processing and programming language processing.

How to access and use this model?

WizardCoder is an open-source framework that can be accessed and used by anyone who is interested in generating and optimizing code using natural language instructions. There are several ways to access and use WizardCoder:

• Online demo: You can try out WizardCoder online by using the demo link. You can enter your natural language instruction in the input box and click on “Submit”. You will see the generated code snippet in the output box.
• GitHub repository: You can download the source code and the pre-trained model of WizardCoder from this GitHub repository. You can also find the instructions on how to install and run WizardCoder locally on your machine.
• Hugging Face model hub: You can access the pre-trained model of WizardCoder from this Hugging Face model hub. You can also use the Hugging Face API to interact with WizardCoder programmatically.

WizardCoder is licensed under the Apache License 2.0, which means that you can use it for both academic and commercial purposes, as long as you follow the terms and conditions of the license.

If you are interested in learning more about WizardCoder framework, please find all relevant links under 'source' section at the end of this article.

Limitations

WizardCoder is a state-of-the-art framework that empowers CLLMs with Evol-Instruct, but it is not perfect. It still has some limitations that need to be addressed in future work, such as:

• WizardCoder relies on the quality and diversity of the pre-trained CLLM, which may not cover all the possible code domains and scenarios. WizardCoder may need to be fine-tuned or adapted to specific code tasks or languages.
• WizardCoder uses natural language instructions as a way to control the code generation process, but natural language is inherently ambiguous and vague. WizardCoder may not be able to interpret or execute some instructions correctly or consistently.
• WizardCoder uses evolutionary algorithms to guide the code generation and optimization process, but evolutionary algorithms are stochastic and heuristic. WizardCoder may not be able to guarantee the optimality or convergence of the generated code.
• WizardCoder optimizes the generated code for functionality, style, and quality simultaneously, but these criteria may not be well-defined or universally agreed upon. WizardCoder may need to incorporate user feedback or preferences to customize the optimization process.

Conclusion

WizardCoder is a breakthrough in the field of natural language processing and programming language processing. It bridges the gap between natural and programming languages and makes code generation more accessible and reliable. WizardCoder has many potential applications and implications for various domains and industries, such as software engineering, education, entertainment, security, etc. WizardCoder is a wizard that can turn your natural language instructions into magical code!

source
research paper - https://arxiv.org/abs/2306.08568
GitHub Repo - https://github.com/nlpxucan/WizardLM/tree/main/WizardCoder
Model weights - https://huggingface.co/WizardLM/WizardCoder-15B-V1.0
demo link - https://e5eaf7d09cc1521c.gradio.app/