ARES: An Automated Evaluation Framework for RAG Systems
A groundbreaking framework for automatically evaluating Retrieval-Augmented Generation (RAG) models.
Get Started Go to Github Read the PaperGet Started
ARES (Automated RAG Evaluation System) simplifies evaluating retrieval-augmented generation (RAG) systems. It automates the assessment of context relevance, answer faithfulness, and answer relevance, eliminating the need for extensive human annotations. ARES generates synthetic training data and fine-tunes lightweight language models to provide accurate evaluations quickly and efficiently. With ARES, you can ensure your RAG systems perform optimally with minimal effort. Get started now to streamline your evaluation process and improve your RAG solutions.
Go to Github
ARES is open-source and available on GitHub, providing you with full access to the codebase. Whether you're looking to understand how ARES works, contribute to its development, or customize it to better suit your specific needs, our GitHub repository is the perfect place to start. By joining our community, you can collaborate with other developers, share your insights, and help enhance the capabilities of ARES. The open-source nature of the project ensures that you can freely modify and adapt it to fit various use cases. Click the button to get started with ARES on GitHub and become a part of our growing community.
Read the Paper
Dive deeper into the details of ARES by reading our comprehensive research paper. Discover the methodologies, evaluations, and innovations that make ARES a powerful tool for automated RAG system evaluation. The paper provides in-depth insights into how ARES generates synthetic data, fine-tunes language models, and leverages prediction-powered inference for accurate and efficient assessments. Click the button to access the full paper and explore the technical intricacies and empirical results of ARES.