| # LLM4PH: Large Language Models as Topological Thinkers | |
| This repository contains the benchmark code for our NeurIPS paper: "Large Language Models as Topological Thinkers: A Benchmark on Graph Persistent Homology". | |
| ## Overview | |
| LLM4PH is a comprehensive benchmark designed to evaluate the capabilities of Large Language Models (LLMs) in understanding and reasoning about topological concepts, specifically focusing on graph persistent homology. | |
| ## Dataset | |
| The benchmark consists of four difficulty levels of tasks: | |
|  | |
| Each level is designed to progressively challenge the model's understanding of topological concepts. | |
| ## Code Structure | |
| The codebase is organized as follows: | |
| ``` | |
| LLM4PH/ | |
| ├── config.py # Configuration settings for tasks and models | |
| ├── main.py # Main entry point for running the benchmark | |
| ├── datasets/ # Dataset files for different difficulty levels | |
| ├── evaluate_code/ # Evaluation scripts and metrics | |
| ├── results/ # Directory for storing evaluation results | |
| └── .env # Environment variables for API keys (create if needed) | |
| ``` | |
| Key components: | |
| - `config.py`: Configure task parameters and model settings | |
| - `main.py`: Run the benchmark with specified configurations | |
| - `evaluate_code/`: Contains evaluation logic and scoring metrics | |
| - `datasets/`: Stores the benchmark datasets | |
| - `results/`: Output directory for evaluation results | |
| ## Installation | |
| Install dependencies: | |
| ```bash | |
| pip install -r requirements.txt | |
| ``` | |
| ## Configuration | |
| The benchmark can be configured through `config.py`: | |
| - Task configuration: Set difficulty levels and evaluation parameters | |
| - Model configuration: Choose between local and API-based models | |
| ### API Key Setup | |
| For closed-source models, create a `.env` file in the root directory: | |
| ```bash | |
| touch .env | |
| ``` | |
| Add your API keys to the `.env` file: | |
| ``` | |
| OPENAI_API_KEY=your_key_here | |
| ANTHROPIC_API_KEY=your_key_here | |
| ``` | |
| ## Usage | |
| 1. Configure your desired task and model in `config.py` | |
| 2. Run the benchmark: | |
| ```bash | |
| python main.py | |
| ``` | |
| ## Citation | |
| If you use this benchmark in your research, please cite our paper: | |
| ``` | |
| ``` | |