ArticleScientific reports2025
A high precision and speed question answering system about the post-COVID-19.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Abstract
Coping with the mutation of COVID-19 and potential future pandemic pathogens is crucial in the post-COVID-19 era. The internet's redundant data poses challenges for accurate and timely information retrieval, with current information retrieval systems often neglecting timeliness and speed. This paper proposes a Reader-Retriever architecture to design a question answering system with high accuracy and speed. We employ a distillation and fine-tuning process on a pre-trained model for the Reader pipeline, accomplished via joint training. Additionally, we propose a high-speed search algorithm IVF-PSQ within the Retriever pipeline. The system achieves state-of-the-art results with a Precision of 84.91%, an Exact Match (EM) score of 71.70%, a F1 score of 63.75%, a Recall of 51.04% and an average search time of 0.407 s.
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