R-Eval: A Unified Toolkit for Evaluating Domain Knowledge of Retrieval Augmented Large Language Models

TL;DR

The paper introduces R-Eval, a comprehensive Python toolkit for evaluating Retrieval-Augmented Large Language Models across various domains and tasks, addressing limitations of existing evaluation methods.

Contribution

It presents a modular, extensible toolkit supporting multiple RAG workflows and customized domain testing, enabling in-depth evaluation of RALLMs.

Findings

Significant variation in RALLMs effectiveness across tasks and domains

Evaluation highlights the importance of task and domain considerations in RAG workflows

R-Eval facilitates systematic and customizable assessment of RALLMs

Abstract

Large language models have achieved remarkable success on general NLP tasks, but they may fall short for domain-specific problems. Recently, various Retrieval-Augmented Large Language Models (RALLMs) are proposed to address this shortcoming. However, existing evaluation tools only provide a few baselines and evaluate them on various domains without mining the depth of domain knowledge. In this paper, we address the challenges of evaluating RALLMs by introducing the R-Eval toolkit, a Python toolkit designed to streamline the evaluation of different RAG workflows in conjunction with LLMs. Our toolkit, which supports popular built-in RAG workflows and allows for the incorporation of customized testing data on the specific domain, is designed to be user-friendly, modular, and extensible. We conduct an evaluation of 21 RALLMs across three task levels and two representative domains, revealing significant variations in the effectiveness of RALLMs across different tasks and domains. Our analysis emphasizes the importance of considering both task and domain requirements when choosing a RAG workflow and LLM combination. We are committed to continuously maintaining our platform at https://github.com/THU-KEG/R-Eval to facilitate both the industry and the researchers.