# GhostCite: A Large-Scale Analysis of Citation Validity in the Age of Large Language Models

arXiv: https://arxiv.org/abs/2602.06718 · HTML: https://mlsanity.com/paper/2602.06718/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 68 references that are in the map.
- First posted on arXiv 6 Feb 2026.

## Builds on (most cited first, 13 of 13)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Qwen Technical Report](https://mlsanity.com/paper/2309.16609/) (2023)
- [TruthfulQA: Measuring How Models Mimic Human Falsehoods](https://mlsanity.com/paper/2109.07958/) (2021)
- [Survey of Hallucination in Natural Language Generation](https://mlsanity.com/paper/2202.03629/) (2022)
- [A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions](https://mlsanity.com/paper/2311.05232/) (2023)
- [DetectGPT: Zero-Shot Machine-Generated Text Detection using Probability Curvature](https://mlsanity.com/paper/2301.11305/) (2023)
- [A Watermark for Large Language Models](https://mlsanity.com/paper/2301.10226/) (2023)
- [Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Models](https://mlsanity.com/paper/2309.01219/) (2023)
- [HaluEval: A Large-Scale Hallucination Evaluation Benchmark for Large Language Models](https://mlsanity.com/paper/2305.11747/) (2023)
- [Troubling Trends in Machine Learning Scholarship](https://mlsanity.com/paper/1807.03341/) (2018)
- [Artificial Intelligence Index Report 2025](https://mlsanity.com/paper/2504.07139/) (2025)
- [ChatGPT and a New Academic Reality: Artificial Intelligence-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing](https://mlsanity.com/paper/2303.13367/) (2023)
- [HalluCitation Matters: Revealing the Impact of Hallucinated References with 300 Hallucinated Papers in ACL Conferences](https://mlsanity.com/paper/2601.18724/) (2026)

## Built on by (most cited first, 11 of 11)

- [Detecting and Correcting Reference Hallucinations in Commercial LLMs and Deep Research Agents](https://mlsanity.com/paper/2604.03173/) (2026)
- [Artifact-centered Claim-aware Observability for Autonomous Scientific Agents](https://mlsanity.com/paper/2608.18312/) (2026)
- [BibTeX Citation Errors in Scientific Publishing Agents: Evaluation and Mitigation](https://mlsanity.com/paper/2604.03159/) (2026)
- [HALLMARK: How to Diagnose LLM Citation Verifiers and When to Deploy Them](https://mlsanity.com/paper/2607.18360/) (2026)
- [Phantom References: Hallucinated Citations That Survive Peer Review at Top-Tier Conferences](https://mlsanity.com/paper/2607.00738/) (2026)
- [sciwrite-lint: Verification Infrastructure for the Age of Science Vibe-Writing](https://mlsanity.com/paper/2604.08501/) (2026)
- [How unique are hallucinated citations offered by generative Artificial Intelligence models?](https://mlsanity.com/paper/2604.16407/) (2026)
- [Where Fake Citations Are Made: Tracing Field-Level Hallucination to Specific Neurons in LLMs](https://mlsanity.com/paper/2604.18880/) (2026)
- [Verified Misguidance: Measuring Structural Citation Failures in Search-Augmented LLMs](https://mlsanity.com/paper/2605.28565/) (2026)
- [Is this Citation on Point?](https://mlsanity.com/paper/2608.12571/) (2026)
- [PaperDoctor: Evidence-Grounded and Actionable Feedback for Scientific Papers in Progress](https://mlsanity.com/paper/2609.16995/) (2026)

## Abstract (arXiv)

Citations provide the basis for trusting scientific claims; when they are invalid or fabricated, this trust collapses. With the advent of Large Language Models (LLMs), this risk has intensified: LLMs are increasingly used for academic writing, but their tendency to fabricate citations (``ghost citations'') poses a systemic threat to citation validity. To quantify this threat, we develop citeb, an open-source framework for large-scale citation verification, and conduct a comprehensive study of citation validity in the LLM era through three complementary experiments. First, we benchmark 13 LLMs on citation generation task in various research domains, finding that all models hallucinate citations at rate from 14.23% to 94.93%. Second, we analyze 2.2 million citations from 56,381 papers at AI/ML and Security venues (2020--2025), finding that 1.07% of papers contain invalid citations, with an 80.9% increase in 2025. Third, we survey 97 researchers, finding that 87.2% use AI-powered tools in their workflows, 76.7% of reviewers do not thoroughly check references, and 74.5% view peer review as ineffective at catching citation errors. Based on these findings, we argue that ghost citations represent a systemic threat to academic integrity, and call for coordinated efforts from community to address this challenge.