OmniBench-RAG: A Multi-Domain Evaluation Platform for Retrieval-Augmented Generation Tools
Year: 2026
Type: conference-paper
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.
Authors: Jiaxuan Liang, Shide Zhou, Wang Kailong
Institution: Huazhong University of Science and Technology
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4415190889