HalluCiteChecker: A Lightweight Toolkit for Hallucinated Citation Detection and Verification in the Era of AI Scientists

2026-04-29

AI Summary

This study addresses the prevalence of fabricated citations—commonly referred to as “hallucinated references”—in AI-generated academic texts, which undermines scholarly credibility and imposes substantial manual verification burdens. To tackle this issue, the work formalizes hallucinated citation detection as a natural language processing task and introduces a lightweight, fully offline open-source toolkit. Requiring only CPU resources and no internet or GPU access, the proposed method enables rapid validation of citation authenticity within seconds on an ordinary laptop. Released as a PyPI package, this solution significantly reduces the verification costs for both reviewers and authors, thereby offering a reliable safeguard for AI-assisted academic writing.

Abstract

We introduce HalluCiteChecker, a toolkit for detecting and verifying hallucinated citations in scientific papers. While AI assistant technologies have transformed the academic writing process, including citation recommendation, they have also led to the emergence of hallucinated citations that do not correspond to any existing work. Such citations not only undermine the credibility of scientific papers but also impose an additional burden on reviewers and authors, who must manually verify their validity during the review process. In this study, we formalize hallucinated citation detection as an NLP task and provide a corresponding toolkit as a practical foundation for addressing this problem. Our package is lightweight and can perform verification in seconds on a standard laptop. It can also be executed entirely offline and runs efficiently using only CPUs. We hope that HalluCiteChecker will help reduce reviewer workload and support organizers by enabling systematic pre-review and publication checks. Our code is released under the Apache 2.0 license on GitHub and is distributed as an installable package via PyPI. A demonstration video is available on YouTube.

Problem

• hallucinated citations
• scientific writing
• AI-generated content
• citation verification
• academic integrity

Innovation

• hallucinated citation detection
• lightweight NLP toolkit
• offline verification
• AI-assisted academic writing
• scientific integrity