Benchmarking Large Language Models in Retrieval-Augmented Generation

TL;DR

This paper systematically evaluates how large language models perform in retrieval-augmented generation tasks, identifying key challenges and establishing a new benchmark to guide future improvements in LLM capabilities.

Contribution

It introduces the Retrieval-Augmented Generation Benchmark (RGB) for comprehensive evaluation of LLMs across fundamental abilities relevant to RAG.

Findings

LLMs show moderate noise robustness

Struggle with negative rejection and information integration

Significant challenges remain for effective RAG application

Abstract

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.