LOCALRQA introduces an open-source toolkit for building retrieval-augmented question-answering systems. It offers various training algorithms, evaluation methods, and deployment tools curated from recent research. The toolkit enables researchers and developers to customize model training, testing, and deployment processes efficiently. LOCALRQA showcases the development of QA systems using online documentation from Databricks and Faire's websites. The performance of models trained with LOCALRQA is comparable to using OpenAI's models text-ada-002 and GPT-4-turbo.
The toolkit supports data generation, retriever training, generative model training, system assembly, evaluation, and deployment. It includes features like generating RQA data from documents, training retrievers with various methods like distillation and contrastive learning, fine-tuning generative models with supervised techniques or fusion-in-decoder approaches. Additionally, it provides automatic evaluation metrics such as Recall@k and ROUGE for assessing system performance.
LOCALRQA also offers two deployment methods: a static evaluation webpage for direct assessment of system performance and an interactive chat webpage for user interaction feedback. Integration with acceleration frameworks enhances document retrieval speed and LLM inference efficiency. The toolkit's flexibility allows users to experiment with different models and algorithms to develop cost-effective RQA systems locally.
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by Xiao Yu,Yuna... at arxiv.org 03-05-2024
https://arxiv.org/pdf/2403.00982.pdfDeeper Inquiries