File size: 2,263 Bytes
9f50319 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 | # 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:
```
```
|