Hong Kong, China – In a significant leap forward for AI-assisted software development, a team led by Professor Jia Jia-ya at the Chinese University of Hong Kong (CUHK) has introduced MoTCoder, an innovative system designed to tackle complex coding challenges with unprecedented accuracy. The system, detailed in a recently published paper, leverages a novel Module-of-Thought (MoT) approach to instruction tuning, enabling large language models (LLMs) to generate more accurate and maintainable code for intricate tasks.
The research, spearheaded by first author Li Jingyao, a Ph.D. student at CUHK’s DV Lab under Professor Jia, addresses a critical limitation of current LLMs in code generation. While LLMs have become adept at basic coding tasks, their performance often falters when confronted with complex problems such as algorithm competitions or enterprise-level system development. The resulting code can be monolithic and difficult to understand or a disorganized soup that is challenging to maintain.
The challenge lies in enabling LLMs to think like engineers, breaking down complex problems into manageable, modular components, explains Li Jingyao.
MoTCoder achieves this through MoT Instruction Tuning, which encourages the LLM to decompose complex tasks into smaller, independent modules. This modular approach not only improves the accuracy of the generated code but also enhances its readability and maintainability.
The effectiveness of MoTCoder was rigorously tested on established programming benchmarks such as APPS and CodeContests. The results demonstrated a significant improvement in performance, with MoTCoder achieving a pass@1 accuracy that surpasses existing state-of-the-art (SOTA) models by up to 6%. This breakthrough signifies a major step towards AI systems that can not only generate code but also exhibit a level of human intelligence in their approach to complex problem-solving.
The research team, including Chen Pengguang and Xia Bin, all members of the DV Lab, believes that MoTCoder has the potential to revolutionize the software development process. By automating the generation of modular and maintainable code, MoTCoder can significantly reduce the workload of human programmers, allowing them to focus on higher-level design and innovation.
The implications of this research extend beyond the realm of software development. The MoT approach could be applied to other complex problem-solving domains, such as robotics, engineering design, and scientific discovery.
The paper, titled MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging, is available for review. The team hopes that their work will inspire further research into the development of more intelligent and capable AI systems.
References:
- Li, J., Chen, P., Xia, B., et al. (2024). MoTCoder: Elevating Large Language Models with Modular of Thought for Challenging. [Insert Publication Details Here, Once Available]
About the Research Team:
The research was conducted at the DV Lab at the Chinese University of Hong Kong, led by Professor Jia Jia-ya. The team’s research focuses on large language models, including model pre-training, post-training, and inference optimization.
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