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1 | A. Author. Grading explanations of problem-solving process and generating feedback using large language models. Physical Review Physics Education Research, 21(1):010126, 2024. | Recoverable | {
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11 | Dmitry Polykovskiy, Konstantin Zubov, Leonid Khristoforov, Michael Gastegger, Andrey Leshchev, Alexander Timoshenko, and Matthias Rupp. Neural scaling of deep chemical models. Nature Machine Intelligence, 5(5):450–460, 2023. | Recoverable | {
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} | Nathan C. Frey, Ryan Soklaski, Simon Axelrod, Siddharth Samsi, Rafael Gómez-Bombarelli, Connor W. Coley, and Vijay Gadepally. Neural scaling of deep chemical models. Nature Machine Intelligence, 2023. |
12 | Donghyeon Kim, Sungho Park, Jaewook Lee, and Minho Choi. Multi-modal molecule structure-text model for text-based retrieval and editing. Nature Machine Intelligence, 5:1123–1132, 2023. | Recoverable | {
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13 | Emad Latif et al. Physicsassistant: An llm-powered interactive learning robot for physics lab investigations. arXiv preprint arXiv:2403.18721, 2024. | Recoverable | {
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14 | Enrique Mencia and Johannes Furnkranz. Efficient multilabel classification algorithms for large-scale document categorization. In ECML PKDD, 2008. | Recoverable | {
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21 | Herbert Surden. Machine Learning and Law. Oxford University Press, 2012. | Recoverable | {
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24 | Ilias Chalkidis, Ion Androutsopoulos, and Nikolaos Aletras. Lexglue: A benchmark dataset for legal language understanding in english. arXiv preprint arXiv:2110.00976, 2021. | Recoverable | {
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25 | Ilias Chalkidis, Marios Fergadiotis, Nikolaos Malakasiotis, and Nikolaos Aletras. Legal-bert: The muppets straight out of law school. In Proceedings of the 12th Language Resources and Evaluation Conference, pages 850–857. European Language Resources Association, 2020. | Recoverable | {
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27 | Jane Lin et al. Question rephrasing for quantifying uncertainty in large language models. arXiv preprint arXiv:2408.03732, 2024. | Recoverable | {
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28 | Jian Li, Xinqiao Tan, Yu Wang, and Zhen Chen. Unified deep learning model for multi-task reaction predictions (t5chem). Journal of Chemical Information and Modeling, 2022. | Recoverable | {
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29 | Jian Wang, Ying Zhang, Min Xu, and Xiaohui Sun. Pushing the boundaries of molecular property prediction for drug discovery with multi-task learning bert enhanced by smiles enumeration. Journal of Chemical Information and Modeling, 2022. | Recoverable | {
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32 | John Doe and Jane Smith. Large language models in politics and democracy: A comprehensive survey. Political Science Review, 58(2):123–145, 2024. | Recoverable | {
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33 | John Smith and Jane Doe. Augmented legal reasoning for case outcome prediction. arXiv:2401.15770 [cs.CL], 2024. https://arxiv.org/abs/2401.15770. | Unrecoverable | null | null |
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38 | Max Planck Institute for Iron Research. Langsim - large language model interface for atomistic simulation. https://www.mpie.de/5063016/LangSim, 2024. | Recoverable | {
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39 | Metric Coders. Exploring the role of large language models (llms) in physics. Metric Coders Blog, 2024. https://www.metriccoders.com/post/exploring-the-role-of-large-language-models-llms-in-physics. | Recoverable | {
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45 | Sandeep Patel, Rohan Mehta, and Anjali Gupta. Protst: Multi-modality learning of protein sequences and biomedical texts. arXiv preprint arXiv:2301.12040, 2023. | Recoverable | {
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48 | Tom Harle et al. Large language models (llms) in engineering education. Information, 15(6):345, 2024. | Recoverable | {
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52 | Xia Zhang, Yuxin Li, Jun Wang, Lei Chen, and Hui Xu. Smiles-bert: Large scale unsupervised pre-training for molecular property prediction. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020. | Recoverable | {
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53 | Xinqiao Tan, Jian Li, Zhen Chen, and Yu Wang. Reactiont5: A large-scale pre-trained model towards application of reaction prediction. arXiv preprint arXiv:2311.06708, 2023. | Recoverable | {
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"venue": "arXiv",
"year": 2023
} | David Rein, Betty Li Hou, Asa Cooper Stickland, Jackson Petty, Richard Yuanzhe Pang, Julien Dirani, Julian Michael, and Samuel R. Bowman. GPQA: A Graduate-Level Google-Proof Q&A Benchmark. arXiv preprint arXiv:2311.12022, 2023. |
93 | Andrew Chen, Andy Chow, Aaron Davidson, Arjun DCunha, Ali Ghodsi, Sue Ann Hong, Andy Konwinski, Clemens Mewald, Siddharth Murching, Tomas Nykodym, et al. Mlflow: A platform for managing the machine learning lifecycle. In Proceedings of the Fourth International Workshop on Data Management for End-to-End Machine Learning... | Unrecoverable | null | null |
94 | Xuelin Chen, Yue Wang, and Zhen Yang. Crule: A common representation framework for unified rul estimation and domain adaptation, 2024a. Referenced in benchmarking literature. | Unrecoverable | null | null |
95 | Winston Yu, Albert Jiang, Jiahe Zhang, Zhihong Xu, Shen Wang, Yiming Xu, He He, Quanquan Gu, and Weikun Shi. NuminaMath: A new frontier for LLM mathematical reasoning. arXiv preprint arXiv:2406.00762, 2024. | Unrecoverable | null | null |
96 | Yongbo Chen, Dong Zhang, Hamid Reza Karimi, Huaicheng Yan, and Wei Xue. Remaining useful life prediction based on deep learning: A survey. Sensors, 24(11):3454, 2024b. | Recoverable | {
"title": "Remaining Useful Life Prediction Based on Deep Learning: A Survey",
"authors": [
"Fuhui Wu",
"Qingbo Wu",
"Yusong Tan",
"Xinghua Xu"
],
"venue": "Sensors",
"year": 2024
} | Fuhui Wu, Qingbo Wu, Yusong Tan, and Xinghua Xu. Remaining Useful Life Prediction Based on Deep Learning: A Survey. Sensors, 2024. |
97 | Yan Huang, Yuxuan Liu, Chao Huang, Jingxiang Zhu, and Naixue Xiong. Stagnn: Spatial-temporal aware graph neural network with multi-perspective feature fusion for remaining useful life prediction. arXiv preprint arXiv:2401.15964, 2024. | Unrecoverable | null | null |
98 | Marc-André Hoff, Thomas Weyrauch, Tim Winsel, Lennart Hagge, Michael Grottke, Enock Nyirenda, and Robert H. Schmitt. Automated machine learning for remaining useful life predictions. arXiv preprint arXiv:2306.12215, 2023. | Recoverable | {
"title": "Automated Machine Learning for Remaining Useful Life Predictions",
"authors": [
"Marc-André Zöller",
"Fabian Mauthe",
"Peter Zeiler",
"Marius Lindauer",
"Marco F. Huber"
],
"venue": "2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC)",
"year": 2023
} | Marc-André Zöller, Fabian Mauthe, Peter Zeiler, Marius Lindauer, and Marco F. Huber. Automated Machine Learning for Remaining Useful Life Predictions. 2023 IEEE International Conference on Systems, Man, and Cybernetics (SMC), 2023. |
99 | Laboratory of Rotating Machinery Diagnostics, UFRJ. Mechanical fault simulator (mfs) vibration dataset. https://www02.smt.ufrj.br/ offshore/mfs/page_01.html, 2000. Accessed: 2025-05-05. | Unrecoverable | null | null |
100 | Haixu Liu, Qingsong Wen, Tian Zhou, Ziqing Ma, Xue Wang, Liang Sun, and Rong Jin. Pyraformer: Low-complexity pyramidal attention for long-range time series modeling and forecasting. In International Conference on Learning Representations (ICLR), 2021a. | Recoverable | {
"title": "Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting",
"authors": [
"Shizhan Liu",
"Hang Yu",
"Cong Liao",
"Jianguo Li",
"Weiyao Lin",
"Alex X. Liu",
"S. Dustdar"
],
"venue": "International Conference on Learning Representatio... | Shizhan Liu, Hang Yu, Cong Liao, Jianguo Li, Weiyao Lin, Alex X. Liu, and S. Dustdar. Pyraformer: Low-Complexity Pyramidal Attention for Long-Range Time Series Modeling and Forecasting. International Conference on Learning Representations, 2022. |
End of preview. Expand in Data Studio
CiteResolve
423 real-world hallucinated citations annotated for source recoverability: 252 Recoverable with a verified
corrected citation, 171 Unrecoverable.
Benchmark of CiteANCHOR: Source-Aware Grounding for Hallucinated Citation Detection and Resolution.
Code and scorer: https://github.com/oldwizard7/CiteANCHOR_code
| Field | Content |
|---|---|
id |
1–423 |
citation |
the hallucinated citation as it appeared |
label |
Recoverable / Unrecoverable |
source |
gold title, authors, venue, year; url for web resources; null if Unrecoverable |
corrected_citation |
the corrected reference (Recoverable only) |
citeresolve.json holds the same rows under data, as read by the scorer.
@misc{tong2026citeanchorsourceawaregroundinghallucinated,
title={CiteAnchor: Source-Aware Grounding for Hallucinated Citation Detection and Resolution},
author={Chenyi Tong and Ping Nie and Yu Zhang},
year={2026},
eprint={2610.30355},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2610.30355},
}
Annotations are released under CC BY 4.0.
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