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LLM Application for coding assistant

Paper Title​

Magicoder: Source Code Is All You Need

Authors​

Yuxiang Wei et. al.

Affiliations​

UIUC et. al

Date​

Dec, 2023

Abstract​

We introduce Magicoder, a series of fully open-source (code, weights, and data) Large Language Models (LLMs) for code that significantly closes the gap with top code models while having no more than 7B parameters. Magicoder models are trained on 75K synthetic instruction data using OSS-INSTRUCT, a novel approach to enlightening LLMs with open-source code snippets to generate high-quality instruction data for code. Our main motivation is to mitigate the inherent bias of the synthetic data generated by LLMs by empowering them with a wealth of opensource references for the production of more diverse, realistic, and controllable data. The orthogonality of OSS-INSTRUCT and other data generation methods like Evol-Instruct further enables us to build an enhanced MagicoderS. Both Magicoder and MagicoderS substantially outperform state-of-the-art code models with similar or even larger sizes on a wide range of coding benchmarks, including Python text-to-code generation, multilingual coding, and data-science program completion. Notably, MagicoderS-CL-7B based on CODELLAMA even surpasses the prominent ChatGPT on HumanEval+ (66.5 vs. 65.9 in pass@1). Overall, OSS-INSTRUCT opens a new direction for low-bias and high-quality instruction tuning using abundant open-source references.

5Ws​

1. What is the problem?​

The problem addressed by "Magicoder" is the limitation in existing Large Language Models (LLMs) for code generation. Traditional LLMs, even state-of-the-art models, tend to inherit biases from synthetic data generated by LLMs and rely on a narrow range of predefined tasks or heuristics. This limitation affects the diversity, realism, and controllability of the code generated by these models.

2. Why is the problem important?​

The problem is important because code generation LLMs are extensively used in real-world software development. Improving these models' ability to generate diverse, realistic, and controllable code is crucial for enhancing the efficiency and effectiveness of software development processes. This improvement directly impacts the quality of applications and systems being developed, thereby influencing the technological landscape.

3. Why is the problem difficult?​

The problem is difficult because it involves mitigating inherent biases in synthetic data used for training LLMs, which is a complex task. Additionally, ensuring that the LLMs can generate code that is not only diverse and realistic but also controllable and tailored to specific programming scenarios adds to the challenge. This requires a sophisticated understanding of both machine learning and software engineering principles.

4. What are the old techniques?​

Old techniques include symbolic approaches like abstraction-based synthesis and programming by examples for domain-specific tasks. Recently, LLMs trained on code, such as GPT-3.5 Turbo and GPT-4, dominated various code generation benchmarks. Techniques like SELF-INSTRUCT and Evol-Instruct were used to improve LLMs' coding abilities. These methods involved using stronger models to generate synthetic coding instructions for training weaker models, a process known as knowledge distillation.

5. Advantages and disadvantages of the new techniques?​

Advantages:

  • Diversity and Realism: Magicoder introduces a novel approach, OSS-INSTRUCT, to generate coding instructions. It uses open-source code snippets, leading to more diverse and realistic data.
  • Controllability: By leveraging real-world code, Magicoder can produce more contextually relevant and controllable outputs.
  • Bias Mitigation: It specifically aims to mitigate the bias inherent in synthetic data generated by traditional LLMs.
  • Performance: Magicoder shows significant improvements over state-of-the-art models in various coding benchmarks, even with fewer parameters.

Disadvantages:

  • Complexity: The approach might be more complex to implement due to the need to source and process a vast range of open-source code snippets.
  • Dependence on Open Source Quality: The effectiveness of the model might be contingent on the quality and variety of the open-source code available.
  • Potential Overfitting: There might be a risk of overfitting to patterns present in open-source projects, although the paper addresses this concern through data decontamination.
6. Conclusion​

Overall, Magicoder presents a significant advancement in the field of code generation using LLMs by addressing the limitations of previous models and introducing new methodologies for generating high-quality, diverse, and controllable code instructions.