arXiv:2605.27700v1 Announce Type: cross Abstract: Large language models (LLMs) are increasingly used to generate scientific reports, but they can produce references that appear plausible while containing corrupted metadata or pointing to papers that do not exist. We introduce CiteCheck, a hybrid framework for citation hallucination detection that verifies whether a citation corresponds to a real scholarly work and whether its metadata is faithful to that work. CiteCheck retrieves candidate publications from external scholarly sources, compares the citation against the retrieved candidate using
The proliferation of LLMs in scientific content generation necessitates immediate solutions to address issues like citation hallucinations, crucial for maintaining academic integrity.
This development addresses a critical vulnerability in the integration of AI into scientific research, safeguarding the reliability and trustworthiness of AI-generated content.
New tools are emerging to validate LLM outputs, shifting from uncritical acceptance to verification, which will influence how LLMs are developed and deployed in sensitive domains.
- · Academic researchers
- · Scientific publishers
- · AI ethicists
- · Developers of verification tools
- · Unscrutinized LLM-generated content
- · Researchers relying on unverified LLM output
Increased trust in LLM-assisted scientific writing due to enhanced verification capabilities.
Development of industry standards for LLM-generated content and citation practices in academia.
A shift in LLM design towards models intrinsically less prone to factual inaccuracies and hallucinations, particularly in reference generation.
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Read at arXiv cs.AI