  Retrieval-Augmented Generation (RAG) offers a promising solution to address
various limitations of Large Language Models (LLMs), such as hallucination and
difficulties in keeping up with real-time updates. This approach is
particularly critical in expert and domain-specific applications where LLMs
struggle to cover expert knowledge. Therefore, evaluating RAG models in such
scenarios is crucial, yet current studies often rely on general knowledge
sources like Wikipedia to assess the models' abilities in solving common-sense
problems. In this paper, we evaluated LLMs by RAG settings in a domain-specific
context, college enrollment. We identified six required abilities for RAG
models, including the ability in conversational RAG, analyzing structural
information, faithfulness to external knowledge, denoising, solving
time-sensitive problems, and understanding multi-document interactions. Each
ability has an associated dataset with shared corpora to evaluate the RAG
models' performance. We evaluated popular LLMs such as Llama, Baichuan,
ChatGLM, and GPT models. Experimental results indicate that existing
closed-book LLMs struggle with domain-specific questions, highlighting the need
for RAG models to solve expert problems. Moreover, there is room for RAG models
to improve their abilities in comprehending conversational history, analyzing
structural information, denoising, processing multi-document interactions, and
faithfulness in expert knowledge. We expect future studies could solve these
problems better.
