OmniBench-RAG: A Multi-Domain Evaluation Platform for Retrieval-Augmented Generation Tools

While Retrieval Augmented Generation (RAG) is widely adopted to enhance LLMs, evaluating its true performance benefits in a reproducible and interpretable way remains challenging. Existing methods often fall short: they lack domain coverage, employ coarse metrics that miss sub-document precision, fail to capture computational trade-offs, and suffer from data leakage in some evaluation datasets (leading to unfair assessment results). Most critically, they provide no standardized framework for comparing RAG effectiveness across different models and domains.