Benchmarking Large Language Models in Retrieval-Augmented Generation

Jiawei Chen, Hongyu Lin, Xianpei Han et al.

2024 · Proceedings of the AAAI Conference on Artificial Intelligence · 312 citations

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, i…

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