Summary of Multi-programming Language Ensemble For Code Generation in Large Language Model, by Tengfei Xue et al.
Multi-Programming Language Ensemble for Code Generation in Large Language Model
by Tengfei Xue, Xuefeng Li, Tahir Azim, Roman Smirnov, Jianhui Yu, Arash Sadrieh, Babak Pahlavan
First submitted to arxiv on: 6 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed Multi-Programming Language Ensemble (MPLE) method leverages the multi-language capabilities of Large Language Models (LLMs) to enhance code generation performance. By treating each language-specific code generation process as an individual “weak expert” and effectively integrating their outputs, MPLE mitigates language-specific errors and biases. This ensemble strategy combines the strengths of different programming languages to produce more accurate and robust code. The approach can be integrated with techniques like the reflection algorithm and Monte Carlo tree search to further improve code quality. Experimental results demonstrate that MPLE enhances baseline performance by up to 17.92% on existing benchmarks, achieving new state-of-the-art results across various LLM models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are great at generating code, especially when they can do it in just one try! But most of the time, these models only work with one type of programming language. This paper thinks: why not use these models to generate code in multiple languages at once? By combining the strengths of different languages, we might get even better code generation results! The authors propose a new way to do this called MPLE (Multi-Programming Language Ensemble). It’s like having multiple “weak experts” working together to create something amazing. This approach helps reduce errors and biases that come from using just one language. In experiments, MPLE did even better than the usual methods, getting 96.25% of code generation right on a certain benchmark! |
