Repository: evalplus/evalplus
Issue: 🤗 [REQUEST] - CodeGeeX4
Root cause: Unimplemented Feature Gap

### Model introduction

The CodeGeeX4 model is an advanced AI-based programming assistant created by a team of researchers and engineers from Tsinghua University and Zhipu.AI. This model is designed to facilitate software development processes and is used for a variety of coding-related tasks.

The CodeGeeX4 model is built upon cutting-edge machine learning techniques and natural language processing algorithms. It is trained on a massive dataset of source code from various programming languages, which enables it to understand and generate code with high accuracy.

Key features and applications of the CodeGeeX4 model include:
1. Code Completion: CodeGeeX4 can predict the next sequence of code as a developer types, helping to increase coding speed and reduce the likelihood of errors.
2. Code Translation: The model can translate code between different programming languages, enabling developers to convert codebases from one language to another with ease.
3. Code Refactoring: CodeGeeX4 can suggest improvements to existing code, making it more readable, efficient, and maintainable.
4. Bug Detection: The model can identify potential bugs and vulnerabilities in code, alerting developers to issues that may need to be addressed.
5. Code Review: CodeGeeX4 can assist in the code review process by analyzing code submissions and providing feedback on best practices, potential optimizations, and adherence to coding standards.
6. Natural Language Understanding: The model can understand and respond to natural language queries related to programming tasks, making it a powerful tool for developers seeking information or assistance.
7. Education and Training: CodeGeeX4 can serve as a mentor for beginner programmers, offering guidance and explanations of coding concepts.

Created by a joint effort from Tsinghua University and Zhipu.AI, the CodeGeeX4 model is a testament to the ongoing advancements in AI and its potential to revolutionize the field of software development.


### Model URL

https://huggingface.co/THUDM/codegeex4-all-9b

### Additional information (Optional)

_No response_

### Decontamination

To avoid contamination in their training data for a model like CodeGeeX4, the authors would typically implement a series of data cleaning, filtering, and validation processes. Here are some strategies they might use:

1. **Data Source Scrubbing**: The authors would carefully select the sources of their training data to ensure they are reputable and have the rights to use the data. They would avoid using datasets that might contain copyrighted or proprietary code that could lead to legal issues or contamination.
2. **Copyright and Licensing Checks**: They would check for copyright and licensing information to ensure that the code used for training is either in the public domain, has an appropriate open-source license, or permissions have been granted for its use in training AI models.
3. **Duplicate Removal**: Algorithms would be used to identify and remove duplicate code snippets from the dataset to prevent the model from learning from the same examples multiple times, which could skew its understanding of code diversity.
4. **Quality Control**: The authors would implement quality control measures to filter out low-quality or incorrect code. This could involve using heuristics, code linters, or even leveraging community feedback to identify and remove problematic code.
5. **Data Augmentation**: To ensure diversity and to avoid overfitting to a particular style or pattern, the authors might augment the dataset with synthetic code generated by other models or through transformations that preserve the underlying coding logic.
6. **Community Guidelines**: If the data is sourced from online communities, there would be guidelines in place to prevent the submission of code that does not meet certain standards or that could be considered harmful or biased.
7. **Preprocessing Scripts**: Custom scripts and tools would be used to preprocess the data, removing any personal identifiers, sensitive information, or irrelevant content that could contaminate the training process.
8. **Human Review**: In some cases, human reviewers might be involved to manually inspect and clean the data, especially for edge cases that automated systems might not catch.
9. **Version Control**: By using version control systems, the authors can track changes to the dataset and ensure that any updates or modifications are documented and justified.
10. **Statistical Analysis**: Statistical methods can be employed to analyze the dataset for anomalies or biases that could indicate contamination.
11. **Feedback Loops**: After initial training, the model could be tested on a validation set, and any issues or biases identified could be traced back to the training data for further cleaning.

By implementing these strategies, the authors of CodeGeeX4 can maintain a high-quality training dataset that is free from contamination, ensuring that the model learns from a diverse and accurate representation of code.


### Author

No

### Data

No

### Security

- [X] I confirm that the model is safe to run which is not designed to produce malicious code or content.

### Integrity

- [X] I confirm that the model comes from unique and original work and does not contain any plagiarism.