Year: 2026
Type: conference-paper
Abstract: Large language models (LLMs) have expanded the potential for AI-assisted scientific claim verification, yet existing systems often exhibit unverifiable attributions, shallow evidence mapping, and hallucinated citations.We present SciTrue, a claim verification system providing source-level accountability and evidence traceability.SciTrue links each claim component to explicit, verifiable scientific sources, enabling users to inspect and challenge model inferences, addressing limitations of both generalpurpose and search-augmented LLMs.In a human evaluation of 300 attributions, SciTrue achieves high fidelity in summary traceability, attribution accuracy, and context alignment, substantially outperforming RAG-based baselines such as GPT-4o-search-preview and Perplexity Sonar Pro.These results underscore the importance of principled attribution and context-aware reasoning in AI-assisted scientific verification.A system demo is available at www.scitrue.org.
Authors: Neset Tan, Minghao Li, Mark Gahegan
Institution: University of Auckland
Language: English
Subfield: Sociology and Political Science
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
OpenAlex ID: W7140095479