This work presents and evaluates the AtomGPT reference checker, an open, web-accessible tool that verifies citations against the scholarly literature by combining large-language-model field extraction with structured retrieval from Semantic Scholar.
Abstract
Large language models are now commonly used as partners in scientific writing, and this shift has brought a subtler type of failure: made-up references. Fabricated authors, bogus DOIs, wrongly assigned identifiers, and citations that merge elements from multiple genuine articles are now being inserted into manuscripts at a volume that traditional peer review was never meant to handle. Recent audits reveal that such references have already slipped through the review process and made their way into the published literature, including leading journals and conferences. Automated verification that operates at the speed and scale of modern content production has therefore become a necessary safeguard rather than a convenience. This work presents and evaluates the AtomGPT reference checker (https://atomgpt.org/hallucination_detector), an open, web-accessible tool that verifies citations against the scholarly literature by combining large-language-model field extraction with structured retrieval from Semantic Scholar. For each reference, the tool extracts the bibliographic fields, retrieves the closest matching real papers, and scores the agreement across title, authorship, and venue to produce a graded judgment of whether a citation is trustworthy, partially supported, or likely fabricated. We benchmark the tool against an externally curated set of confirmed hallucinated citations from accepted NeurIPS 2025 papers and find that it reliably flags the great majority of them.
Large language models are increasingly used in academic writing, including for reference generation, raising concerns about hallucinated and unreliable citations. Recent research suggests that this problem is already widespread and is becoming increasingly prevalent in the published literature and at scientific conferences. In this position paper, we review recent studies on hallucinated references and evaluate several currently available tools for detecting problematic references using documents containing hallucinated citations. The tools assessed include CheckIfExist, HalluCiteChecker, Hallucinator, Hallucinated Reference Finder (HalRef), and RefChecker. While these systems can provide useful early warnings in many cases, their performance is limited by reference extraction errors, incomplete metadata, limited database coverage, and inconsistent verification results. We argue that hallucinated and suspicious references have become a real and growing problem for scientific communication, and that more transparent and multi-source detection systems are still needed.
This study proposes a novel conceptual framework and taxonomy for hallucination mitigation in low-code AI environments, integrating retrieval, validation, conflict resolution, and workflow orchestration mechanisms to contribute to the development of more reliable, transparent, and scalable AI systems.
I. K. W. Adnyana, Rosalin Theophilia Tayane, Fahmi Fahmi et al.· EDUKASIA Jurnal Pendidikan d...· 0 citations
The generative artificial intelligence (AI) has been widely used in academic writing, and its hallucination issue becomes problematic when considering the accuracy and credibility of academic writing. The systematic literature review methodology is used in this paper, in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) process to filter articles related to the topic since 2022, with the aim of examining the influence of AI hallucinations on academic writing. The study reveals that AI hallucinations have three kinds of manifestations, which include content distortion, evidence failure and improper argumentation, which comprises factual error, false or irrelevant citation, and superficially coherent arguments lacking evidence. They may destroy the authenticity, transparency, and logic of academic writing. Even if technologies like Retrieval-Augmented Generation (RAG) and Meta-RAG were able to reduce the hallucination effect at least partially, it would not be possible to completely get rid of it. AI need to be considered as an additional tool, and people are still responsible to verify the information and adhere to academic standards. It is crucial to note that developing critical use skills of AI-generated texts by students in educational environments is very important.
A unified toolkit and interactive web UI for detecting contextual hallucinations in retrieval-augmented, agentic, and memory-grounded LLM systems, and as a faithfulness gate within long-term memory systems is demonstrated.
Julia Belikova, Rauf Parchiev, Mikhail Filimonov et al.· 0 citations
A concise two-axis framework that integrates an “intrinsic-extrinsic” distinction in source attribution introduced by Ji et al. with a “faithfulness-factuality” distinction in contextual grounding surveyed is presented, yielding four clearly defined hallucination types applicable across tasks, modalities and architectures.
Misbah Khan, Preston Billion-Polak, T. Khoshgoftaar· IEEE Access· 0 citations
This study aims to examine the phenomenon of hallucinations in large language models (LLMs) within academic contexts, focusing on their manifestations, causes and implications for academic integrity, research quality and responsible artificial intelligence adoption in higher education.
A systematic literature review was conducted in accordance with PRISMA 2020 guidelines. Searches across Scopus, Web of Science and Emerald Insight databases using keywords related to AI hallucination and academic applications, of which 25 peer-reviewed journal articles met the inclusion criteria. Qualitative thematic analysis was performed using NVivo 14 to synthesise evidence on hallucination types, academic applications, impacts and mitigation strategies.
Six recurring types of hallucinations were identified, with fabricated or inaccurate citations emerging as the most prevalent. The findings indicate that hallucinations systematically compromise academic writing quality, distort assessment processes and undermine epistemic trust in scholarly outputs. Variation in hallucination rates across models and disciplines highlights their context-dependent nature. Key contributing factors include probabilistic text generation, limitations in training data, insufficient contextual understanding and the absence of robust verification mechanisms.
It further contributes a structured classification of hallucination types and a multi-layered governance approach to inform institutional policy and responsible AI adoption.
Addressing hallucinations in academic knowledge production is essential for preserving public trust in higher education and safeguarding the societal value of scholarly research.
This study advances existing knowledge by developing an integrated conceptual perspective linking hallucinations to epistemic risk, information integrity and digital trust.
K. Lai, N. Mustaffa, C. Preece et al.· Journal of Science and Techn...· 0 citations