Benchmarking Large Language Models in Retrieval-Augmented Generation

Retrieval-Augmented Generation (RAG) is a promising approach for mitigating the hallucination of large language models (LLMs). However, existing research lacks rigorous evaluation of the impact of retrieval-augmented generation on different large language models, which make it challenging to identify the potential bottlenecks in the capabilities of RAG for different LLMs. In this paper, we systematically investigate the impact of Retrieval-Augmented Generation on large language models. We analyze the performance of different large language models in 4 fundamental abilities required for RAG, including noise robustness, negative rejection, information integration, and counterfactual robustness. To this end, we establish Retrieval-Augmented Generation Benchmark (RGB), a new corpus for RAG evaluation in both English and Chinese. RGB divides the instances within the benchmark into 4 separate testbeds based on the aforementioned fundamental abilities required to resolve the case. Then we evaluate 6 representative LLMs on RGB to diagnose the challenges of current LLMs when applying RAG. Evaluation reveals that while LLMs exhibit a certain degree of noise robustness, they still struggle significantly in terms of negative rejection, information integration, and dealing with false information. The aforementioned assessment outcomes indicate that there is still a considerable journey ahead to effectively apply RAG to LLMs.

Authors

• Jiawei Chen State Key Laboratory of Blockchain and Data Security, Zhejiang University College of Computer Science, Zhejiang University, China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security
• Hongyu Lin Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China
• Xianpei Han Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China
• Le Sun Chinese Information Processing Laboratory, Institute of Software, Chinese Academy of Sciences, Beijing, China

Keywords

• NLP: Interpretability, Analysis, and Evaluation of NLP Models