Unmediated AI-Assisted Scholarly Citations
Stefan Szeider

TL;DR
This paper introduces an architecture combining language models with direct database access to improve the reliability of AI-assisted scholarly citations, ensuring accurate and verified references through a conversational interface.
Contribution
The authors present the Model Context Protocol, enabling language models to search and verify bibliographic data directly from authoritative sources during scholarly interactions.
Findings
System successfully integrates LLM chatbots with DBLP database.
Ensures citation accuracy by bypassing LLM during final data export.
Demonstrates adaptability to various bibliographic databases.
Abstract
Traditional bibliography databases require users to navigate search forms and manually copy citation data. Language models offer an alternative: a natural-language interface where researchers write text with informal citation fragments, which are automatically resolved to proper references. However, language models are not reliable for scholarly work as they generate fabricated (hallucinated) citations at substantial rates. We present an architectural approach that combines the natural-language interface of LLM chatbots with the accuracy of direct database access, implemented through the Model Context Protocol. Our system enables language models to search bibliographic databases, perform fuzzy matching, and export verified entries, all through conversational interaction. A key architectural principle bypasses the language model during final data export: entries are fetched directly…
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Taxonomy
TopicsAI in Service Interactions · Topic Modeling · Biomedical Text Mining and Ontologies
