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

Evaluating RAG systems requires an accurate assessment of their ability to retrieve and generate fact-based content. The FaaF approach introduces a unique method that leverages the functional calling abilities of LMs, overcoming the reliability issues present in earlier methods.

Innovations in Evaluation:

• FaaF improves the detection of unsupported facts in generated text
• The new approach is more efficient and cost-effective than previous prompt-based evaluation methods

The FaaF method marks a significant improvement in how we can evaluate RAG systems, ensuring higher accuracy in the generated outputs.

Assessment FaaF’s novel strategy for evaluating RAG outputs is crucial for maintaining the integrity and reliability of AI-generated content. Its ability to refine the fact-checking process bodes well for the future use of AI in knowledge-intensive fields such as journalism and academia.