Results of the summarizing process for the arXiv paper: 2312.02120v1

The paper introduces Magicoder, a series of fully open-source Large Language Models (LLMs) for code that aim to close the gap with top code models. These models have no more than 7B parameters and are trained on 75K synthetic instruction data using OSS-Instruct, a novel approach that incorporates open-source code snippets to generate high-quality instruction data for code. The main motivation behind this work is to address the inherent bias in synthetic data generated by LLMs by providing them with a wealth of open-source references, resulting in more diverse, realistic, and controllable data. The authors highlight the orthogonality of OSS-Instruct and other data generation methods like Evol-Instruct, which enables the development of an enhanced version called MagicoderS. Both Magicoder and MagicoderS outperform state-of-the-art code models with similar or larger sizes across various coding benchmarks. These benchmarks include Python text-to-code generation, multilingual coding, and data science program completion. Notably, MagicoderS based on CodeLlama even surpasses ChatGPT on HumanEval+ in terms of pass@1 score (66.5 vs 65.9). Overall, OSS-Instruct opens up new possibilities for low bias and high quality instruction tuning using abundant open source references. The authors of the paper are Yuxiang Wei, Zhe Wang, Jiawei Liu, Yifeng Ding and Lingming Zhang.

• Introduces Magicoder, a series of fully open-source Large Language Models (LLMs) for code
• Aim to close the gap with top code models
• Models have no more than 7B parameters and are trained on 75K synthetic instruction data using OSS-Instruct
• OSS-Instruct incorporates open-source code snippets to generate high-quality instruction data for code
• Motivation is to address bias in synthetic data generated by LLMs by providing them with open-source references
• Magicoder and MagicoderS outperform state-of-the-art code models across various coding benchmarks
• Benchmarks include Python text-to-code generation, multilingual coding, and data science program completion
• MagicoderS based on CodeLlama surpasses ChatGPT on HumanEval+ in terms of pass@1 score (66.5 vs 65.9)
• OSS-Instruct enables low bias and high-quality instruction tuning using abundant open source references

SummaryMagicoder is a special computer program that helps with writing code. It uses a lot of information and can understand different languages. It is better than other similar programs because it has been trained on many examples. This training makes it very good at understanding and writing code. MagicoderS is an even better version of Magicoder, and it can do more things like talking to people. Definitions- Large Language Models (LLMs): Special computer programs that can understand and write code. - Parameters: Information that the computer program uses to make decisions. - Synthetic: Made by a computer program instead of being real. - Instruction data: Information that tells the computer program what to do. - Open-source: Computer programs or information that anyone can use and change for free. - Bias: When something is not fair or equal for everyone. - References: Examples or sources of information that the computer program can learn from. - Benchmarks: Tests or challenges to see how well the computer program performs compared to others.

Introducing Magicoder: Open-Source Large Language Models for Code

The world of artificial intelligence (AI) is rapidly advancing, and with it comes the development of large language models (LLMs) for code. These models are used to generate code from natural language instructions, but they have been limited in their capabilities due to the inherent bias in synthetic data generated by LLMs. To address this issue, researchers at Tsinghua University have developed Magicoder, a series of fully open-source LLMs for code that aim to close the gap with top code models.

Magicoder: A Novel Approach to Generating High Quality Instruction Data

Magicoder was developed using OSS-Instruct, a novel approach that incorporates open source code snippets into instruction data generation. This method enables developers to create high quality instruction data that is more diverse and realistic than what can be achieved through traditional methods like Evol-Instruct. The main motivation behind this work is to reduce the bias found in synthetic data generated by LLMs by providing them with a wealth of open source references.

Performance Evaluation

To evaluate performance, the authors tested Magicoder and its enhanced version called MagicoderS on various coding benchmarks including Python text-to-code generation, multilingual coding, and data science program completion. Notably, MagicoderS based on CodeLlama even surpassed ChatGPT on HumanEval+ in terms of pass@1 score (66.5 vs 65.9). Both versions outperformed state-of-the-art code models with similar or larger sizes across all benchmarks tested.

Conclusion

Overall, OSS-Instruct opens up new possibilities for low bias and high quality instruction tuning using abundant open source references as demonstrated by the success of Magicoder and its enhanced version MagicodersS across various coding benchmarks. The authors Yuxiang Wei, Zhe Wang, Jiawei Liu ,Yifeng Ding ,and Lingming Zhang hope that their research will pave the way for further advancements in AI technology related to natural language processing and machine learning applications involving programming languages such as Python or JavaScripts .