Citation Hallucination

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10 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Errors of LLM-Assisted Literature Retrieval in Environmental Science: A Comparison Study of Abstract versus Full-text Based Prompts
Oct 5, 2026Yanjun Chen, Yongfeng Zhang, Lanjing ZhangLLM Prompting LLM Reliability
Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA. · East Brunswick High School, East Brunswick, NJ, USA. · Department of Computer Science, Rutgers University, Piscataway, NJ, USA. +3
MedAgent-R1: Faithfulness-Aware Reinforcement Learning for Evidence-Grounded Medical Reasoning
Aug 31, 2026Jiangwang Chen, Chenghao Zhang, Hengxing CaiLLM Hallucination Mitigation Evidence-Grounded Reasoning
Tsinghua University · DP Technology
AI's Capability in Assisting Scientific Research in Physics, Astrophysics, and Cosmology I: Literature Review
Jul 28, 2026Anamaria Hell, Kateryna Vovk, Veena Krishnaraj +18Astrophysics AI-Assisted Scientific Research
IPMU
CiteVQA: Benchmarking Evidence Attribution for Trustworthy Document Intelligence
May 13, 2026Dongsheng Ma, Jiayu Li, Zhengren Wang +9VLM Evaluation Citation Verification
Peking University · Shanghai Artificial Intelligence Laboratory
Source or It Didn't Happen: A Multi-Agent Framework for Citation Hallucination Detection
May 9, 2026Mingzhe Li, Zhiqiang Lin, Shiqing MaHallucination in Language Models Citation Verification
University of Massachusetts Amherst · The Ohio State University
LLM hallucinations in the wild: Large-scale evidence from non-existent citations
May 8, 2026Zhenyue Zhao, Yihe Wang, Toby Stuart +3LLM Auditing LLM Hallucination Detection
Department of Information Science, Cornell University, Ithaca, NY, USA · Department of Sociology, University of California Los Angeles, Los Angeles, CA, USA · Department of Computer Science and Technology, Tsinghua University, Beijing, China +1
Where Fake Citations Are Made: Tracing Field-Level Hallucination to Specific Neurons in LLMs
Apr 20, 2026Yuefei Chen, Yihao Quan, Xiaodong Lin +1LLM Hallucination Mitigation Hallucination in Language Models
Rutgers University
How unique are hallucinated citations offered by generative Artificial Intelligence models?
Mar 31, 2026Dirk HR SpennemannAcademic Integrity Citation Hallucination
Gulbali Institute; Charles Sturt University; Albury NSW 2640, Australia
ManiBench: A Benchmark for Testing Visual-Logic Drift and Syntactic Hallucinations in Manim Code Generation
Feb 24, 2026Nabin OliCitation Hallucination Code Generation
Sunway College Kathmandu, Birmingham City University
Abstention vs. Hallucination: Benchmarking LLM Source Attribution for Scientific Citations
May 3, 2024Deepa Tilwani, Yash Saxena, Seyedali Mohammadi +5Citation Verification RAG Evaluation
Artificial Intelligence Institute, University of South Carolina, Columbia, South Carolina, USA · University of Maryland, Baltimore County, Baltimore, Maryland, USA · IAIRO, India +1

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Errors of LLM-Assisted Literature Retrieval in Environmental Science: A Comparison Study of Abstract versus Full-text Based Prompts

Yanjun Chen, Yongfeng Zhang, Lanjing Zhang

Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA. · East Brunswick High School, East Brunswick, NJ, USA. · Department of Computer Science, Rutgers University, Piscataway, NJ, USA.+3

LLM Prompting LLM Reliability Information Retrieval Citation Hallucination Scientific Document Retrieval

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Large language models (LLMs) are increasingly used for literature search and synthesis. However, it is unclear whether they retrieve accurate bibliographic information in environmental science. Therefore, we quantitatively compared the errors of widely used LLM platforms in retrieving references related to original articles from five leading environmental science journals (Energy and Environmental Science, Nature Sustainability, Nature Climate Change, Lancet Planetary Health, and Environmental Science and Technology) published in 2024 to 2025. Claude, ChatGPT, Grok, DeepSeek, Perplexity, and Gemini were used as the LLM platforms. LLMs retrieved 10 references for each of the 50 randomly selected original article using either the article's abstract or its full-text as prompt. The retrieved references were subject to a multimetric score ratio combining validity of bibliographic data, Google Scholar link, digital object identifier, Scopus Electronic Identifier and relevance score (cited by or being the index paper), and the proportion of complete fabrication that failed all metrics. Abstract-only prompt yielded significantly higher accuracy than full-text one. This advantage was confirmed in multilevel mixed-effect multivariable regression after adjusting for journal, platform, and output order. Source journal and the position of a reference within the output list were also independently associated with retrieval accuracy, with lower-listed references associated with lower accuracy. These findings suggest that LLM assisted literature retrieval in environmental science remains moderately accurate and overall inconsistent, varying significantly by platform, journal, prompt type, and output position. Abstract-based prompting, as task-aligned information compression, may outperform full-text one in literature retrieval. Caution should be used when generalizing our findings.