LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems
<|reference_start|>LocalRQA: From Generating Data to Locally Training, Testing, and Deploying Retrieval-Augmented QA Systems: Retrieval-augmented question-answering systems combine retrieval techniques with large language models to provide answers that are more accurate and informative. Many existing toolkits allow users to quickly build such systems using off-the-shelf models, but they fall short in supporting researchers and developers to customize the model training, testing, and deployment process. We propose LocalRQA, an open-source toolkit that features a wide selection of model training algorithms, evaluation methods, and deployment tools curated from the latest research. As a showcase, we build QA systems using online documentation obtained from Databricks and Faire's websites. We find 7B-models trained and deployed using LocalRQA reach a similar performance compared to using OpenAI's text-ada-002 and GPT-4-turbo.<|reference_end|>
@article{yu2024localrqa:,
    title={LocalRQA: From Generating Data to Locally Training, Testing, and
  Deploying Retrieval-Augmented QA Systems},
    author={Xiao Yu, Yunan Lu, Zhou Yu},
    journal={arXiv preprint arXiv:2403.00982},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.00982},
    primaryClass={cs.CL}
}