OmniBench-RAG: A Multi-Domain Evaluation Platform for Retrieval-Augmented Generation Tools
Abstract
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. We introduce OmniBench-RAG, a novel automated platform for multi-domain evaluation of RAG systems. It supports user modification of multiple parameters to investigate RAG performance correlations with various factors, including multi-domain evaluation datasets, configurable reasoning settings, curated or user-provided RAG corpora, and flexible retrieval parameters such as Top-K. The platform quantifies performance using two standardized metrics: Improvements (accuracy gains) and Transformation (efficiency differences between pre-RAG and post-RAG models), enabling reproducible comparisons across models and tasks. It also features dynamic test generation, modular evaluation pipelines, and automated knowledge base construction. The evaluation reveals that RAG improvements are not guaranteed; rather, they depend strongly on factors such as corpus–dataset relevance and retrieval configurations, which the platform systematically…
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