EvaluatingRAGsystems,however,presentsseveralchallenges: (1) modular complexity: The modular nature of RAG systems, comprising both a retriever and a generator, complicates the design of effective evaluation metrics. It is crucial to establish metrics that can holistically assess the entire system as well as evaluate the individual modules and their interplay [53],allowing for fully understanding the sources ofthe errors and misses and howthey aregenerated.

The AUCsonthetrainingand are 0.855 and 0.859, respectively.

Foundation models are trained on increasingly immense and opaque datasets.

Deep learning methods have boosted the adoption of NLP systems in real-life applications.

However, if intrinsic parameters change, neural computations are altered drastically.