Magicoder: Source Code Is All You Need (AI summary)

Key Points

- The paper discusses the introduction of Magicoder, a series of fully open-source Large Language Models (LLMs) for code that were trained on 75k synthetic instruction data using OSS-I NSTRUCT.

- The main goal of Magicoder is to mitigate the bias of synthetic data generated by LLMs by empowering them with a wealth of open-source references for the production of more diverse, realistic, and controllable data.

- Magicoder and MagicoderS substantially outperform state-of-the-art code models on a wide range of coding benchmarks, including Python text-to-code generation, multilingual coding, and data-science program completion.

- The paper proposes OSS-I NSTRUCT as a novel data generation method using Large Language Models to generate low-bias and high-quality coding challenges from open-source code snippets.

- OSS-I NSTRUCT leverages a powerful LLM to automatically generate new coding problems by drawing inspiration from any random code snippets collected from open source.

- The study also performs an extensive evaluation on widely used programming languages using the MultiPL-E benchmark, showing that Magicoder-CL significantly improves the base C ODE L LAMA-P YTHON-7B across all studied programming languages.

- MagicoderS results in better performance, potentially indicating the quality of code generation. It also significantly outperforms DeepSeek-Coder-Instruct-6.7B on all the benchmarks with 8× less fine-tuning tokens.

- The paper makes significant contributions by introducing OSS-I NSTRUCT, building the Magicoder series, and fully open-sourcing the model weights, training data, and source code for facilitating future research.

- The results demonstrate the effectiveness of OSS-I NSTRUCT and open the door for future research directions, including applying OSS-I NSTRUCT to larger base models and generating higher-quality data, and extending to more advanced teacher LLMs such as GPT-4.

Summary

LLMs for Code Generation and OSS-INSTRUCT Methodology
The paper discusses the use of Large Language Models (LLMs) for code generation and introduces a novel method called OSS-INSTRUCT to mitigate the inherent bias of LLMs in generating coding instructions. The method leverages open-source code snippets to create diverse and realistic coding challenges, empowering LLMs with a wealth of open-source references. The researchers developed the Magicoder series, which substantially outperforms state-of-the-art code models on various coding benchmarks, including Python text-to-code generation and multilingual coding. The paper also introduces an advanced DeepSeek-Coder series, with exceptional coding performance, and discusses the correlation between the programming languages in the training data and the downstream performance of different languages.

Additionally, the paper evaluates the performance of the proposed method and models on various benchmarks, showcasing significant improvements over existing models. The researchers emphasize the potential of OSS-INSTRUCT for creating low-bias and high-quality instruction-tuning data from open-source references and have fully open-sourced the model weights, training data, and source code to facilitate future research. The evaluation includes comparisons with notable code models and benchmarks, demonstrating the efficacy of the proposed method in enhancing code generation capabilities.

Overview and Significance
Overall, the paper highlights the challenges in code generation, the use of LLMs, the proposed OSS-INSTRUCT method to address bias in code generation, the evaluation of the Magicoder series and the recent advancement in the DeepSeek-Coder series, and the significance of the correlation between training data and performance on various coding benchmarks.

Reference: https://arxiv.org/abs/2312.02120