Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Paper2Code: Automating Code Generation from Scientific Papers in Machine Learning
Paper2Code, also known as PaperCoder, is a multi-agent Large Language Model (LLM) framework designed to automate the generation of functional code repositories from machine learning scientific papers. This innovative tool addresses the common issue where code implementations for research papers are often unavailable, making it challenging for researchers to reproduce results and build upon existing work.
How Paper2Code Works
PaperCoder operates in three distinct stages:
- Planning: Constructs a high-level roadmap, designs system architecture with diagrams, identifies file dependencies, and generates configuration files.
- Analysis: Focuses on interpreting implementation-specific details from the paper.
- Generation: Produces modular, dependency-aware code based on the analysis.
Each stage is handled by specialized agents that collaborate effectively across the pipeline, ensuring high-quality and faithful code implementations.
Evaluation and Performance
PaperCoder has been rigorously evaluated using both model-based and human evaluations, including feedback from the original paper authors. It has demonstrated significant strengths in the PaperBench benchmark, outperforming strong baselines by substantial margins. The tool is particularly effective in generating high-quality code that closely aligns with the original research.
Getting Started with Paper2Code
To use Paper2Code, follow these steps:
- Install the required dependencies:
pip install openai - Clone the repository:
git clone https://github.com/going-doer/Paper2Code.git - Run the example script:
cd scripts && bash run.sh
For detailed setup instructions, including converting PDFs to JSON format, refer to the GitHub repository.
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