FaaF: Facts as a Function for the evaluation of RAG systems

Retrieval Augmented Generation (RAG) systems are critically dependent on factual recall from referenced sources. ‘FaaF,’ or Facts as a Function, introduces an innovative approach that uses Large Models’ function-calling abilities for fact verification, facilitating more accurate RAG evaluation. This method demonstrates superior performance compared to traditional prompt-based evaluations, especially when handling incomplete or inaccurate information.

Key elements of FaaF include:

• Utilizing Large Models’ function-calling abilities for enhanced fact verification.
• Outperforming prompt-based approaches by handling information with higher efficacy.
• Offering a framework for the reliable evaluation of RAG factual recall.
• Achieving efficiency and cost reduction in evaluations over previous methods.

Explore the approach

As RAG systems continue to integrate into various applications, the development of FaaF showcases a substantial improvement in the evaluation of their performance. Enhancing fact verification steps us closer to generating credible and trustworthy information, which is foundational for tasks such as news reporting, academic research, and content creation.