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.
| Comments: | Work In Progress |
| Subjects: | Computation and Language (cs.CL); Artificial Intelligence (cs.AI); Digital Libraries (cs.DL) |
| Cite as: | arXiv:2604.26835 [cs.CL] |
| (or arXiv:2604.26835v1 [cs.CL] for this version) | |
| https://doi.org/10.48550/arXiv.2604.26835 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Yusuke Sakai [view email]
[v1]
Wed, 29 Apr 2026 16:01:42 UTC (496 KB)