RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation
<|reference_start|>RAGLAB: A Modular and Research-Oriented Unified Framework for Retrieval-Augmented Generation: Large Language Models (LLMs) demonstrate human-level capabilities in dialogue, reasoning, and knowledge retention. However, even the most advanced LLMs face challenges such as hallucinations and real-time updating of their knowledge. Current research addresses this bottleneck by equipping LLMs with external knowledge, a technique known as Retrieval Augmented Generation (RAG). However, two key issues constrained the development of RAG. First, there is a growing lack of comprehensive and fair comparisons between novel RAG algorithms. Second, open-source tools such as LlamaIndex and LangChain employ high-level abstractions, which results in a lack of transparency and limits the ability to develop novel algorithms and evaluation metrics. To close this gap, we introduce RAGLAB, a modular and research-oriented open-source library. RAGLAB reproduces 6 existing algorithms and provides a comprehensive ecosystem for investigating RAG algorithms. Leveraging RAGLAB, we conduct a fair comparison of 6 RAG algorithms across 10 benchmarks. With RAGLAB, researchers can efficiently compare the performance of various algorithms and develop novel algorithms.<|reference_end|>
@article{zhang2024raglab:,
    title={RAGLAB: A Modular and Research-Oriented Unified Framework for
  Retrieval-Augmented Generation},
    author={Xuanwang Zhang, Yunze Song, Yidong Wang, Shuyun Tang, Xinfeng Li,
  Zhengran Zeng, Zhen Wu, Wei Ye, Wenyuan Xu, Yue Zhang, Xinyu Dai, Shikun
  Zhang, Qingsong Wen},
    journal={arXiv preprint arXiv:2408.11381},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.11381},
    primaryClass={cs.CL}
}