SemanticCite: Citation Verification with AI-Powered Full-Text Analysis and Evidence-Based Reasoning
Sebastian Haan

TL;DR
SemanticCite is an AI system that verifies citation accuracy by analyzing full texts and providing detailed, evidence-based explanations, addressing challenges like citation errors and AI hallucinations in scientific literature.
Contribution
It introduces a novel AI-powered framework combining multiple retrieval methods and a four-class classification system for nuanced citation verification and provides an open-source dataset and models.
Findings
Lightweight models perform comparably to large systems.
The system achieves high accuracy in classifying citation support levels.
Open-source tools facilitate scalable citation verification.
Abstract
Effective scientific communication depends on accurate citations that validate sources and guide readers to supporting evidence. Yet academic literature faces mounting challenges: semantic citation errors that misrepresent sources, AI-generated hallucinated references, and traditional citation formats that point to entire papers without indicating which sections substantiate specific claims. We introduce SemanticCite, an AI-powered system that verifies citation accuracy through full-text source analysis while providing rich contextual information via detailed reasoning and relevant text snippets. Our approach combines multiple retrieval methods with a four-class classification system (Supported, Partially Supported, Unsupported, Uncertain) that captures nuanced claim-source relationships and enables appropriate remedial actions for different error types. Our experiments show that…
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Taxonomy
TopicsBiomedical Text Mining and Ontologies · Scientific Computing and Data Management · Topic Modeling
