2024EMNLP 2024 Findings资源论文高效 RAG被引 88 次
BERGEN: A Benchmarking Library for Retrieval-Augmented Generation
David Rau, Hervé Déjean, Nadezhda Chirkova, Thibault Formal, Shuai Wang, Stéphane Clinchant, Vassilina Nikoulina
Findings of the Association for Computational Linguistics: EMNLP 2024
摘要
Retrieval-Augmented Generation allows to enhance Large Language Models with external knowledge. In response to the recent popularity of generative LLMs, many RAG approaches have been proposed, which involve an intricate number of different configurations such as evaluation datasets, collections, metrics, retrievers, and LLMs. Inconsistent benchmarking poses a major challenge in comparing approaches and understanding the impact of each component in the pipeline. In this work, we study best practices that lay the groundwork for a systematic evaluation of RAG and present BERGEN, an end-to-end library for reproducible research standardizing RAG experiments. In an extensive study focusing on QA, we benchmark different state-of-the-art retrievers, rerankers, and LLMs. Additionally, we analyze existing RAG metrics and datasets. Our open-source library BERGEN is available under https://github.com/naver/bergen
引用 · BibTeX
@inproceedings{rau2024bergen,
title = {{BERGEN: A Benchmarking Library for Retrieval-Augmented Generation}},
author = {David Rau and Hervé Déjean and Nadezhda Chirkova and Thibault Formal and Shuai Wang and Stéphane Clinchant and Vassilina Nikoulina},
booktitle = {Findings of the Association for Computational Linguistics: EMNLP 2024},
year = {2024},
url = {https://arxiv.org/abs/2407.01102}
}