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Content-based model enhances reference service efficiency in legal bibliographic contexts, suggesting integration with traditional practices.
AI-based Question-Answering (QA) systems offer an efficient means of retrieving information from legal bibliographic datasets. This study proposes a content-based selector model that extracts minimal yet relevant contextual responses to user queries. By incorporating theories of reference service, particularly Bernard F. Vavrek’s holistic approach and S.R. Ranganathan’s principles, this research contextualises AI-driven solutions within traditional library services. The findings demonstrate the robustness of AI in enhancing reference services, supplementing but not replacing human librarians.
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Bhushan et al. (2025) studied this question.
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