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   "citation": "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.",
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     "Brandon R Anderson",
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    "year": 2021
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    "title": "SolvBERT for solvation free energy and solubility prediction: a demonstration of an NLP model for predicting the properties of molecular complexes",
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     "Jiahui Yu",
     "Chengwei Zhang",
     "Yingying Cheng",
     "Yun-Fang Yang",
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   "citation": "Max Planck Institute for Iron Research. Langsim - large language model interface for atomistic simulation. https://www.mpie.de/5063016/LangSim, 2024.",
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    "year": 2023
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   "citation": "R. Wright and M. Miller. Contract Drafting and Negotiation. Oxford University Press, 2013.",
   "label": "Unrecoverable",
   "source": null
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  {
   "id": 42,
   "citation": "Rakesh Karanjai et al. Synthesizing public opinions with llms: Role creation, impacts, and challenges. arXiv preprint arXiv:2504.00241, 2025.",
   "label": "Recoverable",
   "source": {
    "title": "Synthesizing Public Opinions with LLMs: Role Creation, Impacts, and the Future of Edemorcacy",
    "authors": [
     "Rabimba Karanjai",
     "Boris Shor",
     "Amanda Austin",
     "Ryan Kennedy",
     "Yang Lu",
     "Lei Xu",
     "Weidong Shi"
    ],
    "venue": "2025 Eleventh International Conference on eDemocracy & eGovernment (ICEDEG)",
    "year": 2025
   },
   "corrected_citation": "Rabimba Karanjai, Boris Shor, Amanda Austin, Ryan Kennedy, Yang Lu, Lei Xu, and Weidong Shi. Synthesizing Public Opinions with LLMs: Role Creation, Impacts, and the Future of Edemorcacy. 2025 Eleventh International Conference on eDemocracy & eGovernment (ICEDEG), 2025."
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   "citation": "Rodrigo Rabelo, Masatoshi Kaneko, Randy Goebel, and et al. Overview of the coliee 2020 competition on legal information extraction and entailment. In Proceedings of COLIEE 2020, 2020.",
   "label": "Unrecoverable",
   "source": null
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   "id": 44,
   "citation": "Sai Wang, Hang Yuan, Leon Zhou, Lionel Ming shuan Ni, Heung yeung Shum, and Jian Guo. Alpha-gpt: Human-ai interactive alpha mining for quantitative investment. ArXiv, abs/2308.00016, 2023.",
   "label": "Recoverable",
   "source": {
    "title": "Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment",
    "authors": [
     "Saizhuo Wang",
     "Hang Yuan",
     "Leon Zhou",
     "Lionel Ni",
     "Heung-Yeung Shum",
     "Jian Guo"
    ],
    "venue": "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    "year": 2025
   },
   "corrected_citation": "Saizhuo Wang, Hang Yuan, Leon Zhou, Lionel Ni, Heung-Yeung Shum, and Jian Guo. Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investment. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2025."
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  {
   "id": 45,
   "citation": "Sandeep Patel, Rohan Mehta, and Anjali Gupta. Protst: Multi-modality learning of protein sequences and biomedical texts. arXiv preprint arXiv:2301.12040, 2023.",
   "label": "Recoverable",
   "source": {
    "title": "Protst: Multi-modality learning of protein sequences and biomedical texts",
    "authors": [
     "Minghao Xu",
     "Xinyu Yuan",
     "Santiago Miret",
     "Jian Tang"
    ],
    "venue": "International conference on machine learning",
    "year": 2023
   },
   "corrected_citation": "Minghao Xu, Xinyu Yuan, Santiago Miret, and Jian Tang. Protst: Multi-modality learning of protein sequences and biomedical texts. International conference on machine learning, 2023."
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  {
   "id": 46,
   "citation": "Sanguk Lee, Tai-Quan Peng, Matthew H. Goldberg, et al. Can large language models estimate public opinion about global warming? an empirical assessment of algorithmic fidelity and bias. PLOS Climate, 3(6):e0000429, 2024.",
   "label": "Recoverable",
   "source": {
    "title": "Can large language models estimate public opinion about global warming? An empirical assessment of algorithmic fidelity and bias",
    "authors": [
     "Sanguk Lee",
     "Tai-Quan Peng",
     "Matthew H. Goldberg",
     "Seth A. Rosenthal",
     "John Kotcher",
     "Edward Maibach",
     "Anthony Leiserowitz"
    ],
    "venue": "PLOS Climate",
    "year": 2024
   },
   "corrected_citation": "Sanguk Lee, Tai-Quan Peng, Matthew H. Goldberg, Seth A. Rosenthal, John Kotcher, Edward Maibach, and Anthony Leiserowitz. Can large language models estimate public opinion about global warming? An empirical assessment of algorithmic fidelity and bias. PLOS Climate, 2024."
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   "id": 47,
   "citation": "Sanjay Mohapatra, Zhenqin Wu, Guodong Zhang, Ping Men, and Yuedong Liu. Molecule attention transformer. arXiv preprint arXiv:2002.08264, 2020.",
   "label": "Recoverable",
   "source": {
    "title": "Molecule Attention Transformer",
    "authors": [
     "Łukasz Maziarka",
     "Tomasz Danel",
     "Sławomir Mucha",
     "Krzysztof Rataj",
     "Jacek Tabor",
     "Stanisław Jastrzȩbski"
    ],
    "venue": "arXiv (Cornell University)",
    "year": 2020
   },
   "corrected_citation": "Łukasz Maziarka, Tomasz Danel, Sławomir Mucha, Krzysztof Rataj, Jacek Tabor, and Stanisław Jastrzȩbski. Molecule Attention Transformer. arXiv preprint arXiv:2002.08264, 2020."
  },
  {
   "id": 48,
   "citation": "Tom Harle et al. Large language models (llms) in engineering education. Information, 15(6):345, 2024.",
   "label": "Recoverable",
   "source": {
    "title": "Large Language Models (LLMs) in Engineering Education: A Systematic Review and Suggestions for Practical Adoption",
    "authors": [
     "Stefano Filippi",
     "Barbara Motyl"
    ],
    "venue": "Information",
    "year": 2024
   },
   "corrected_citation": "Stefano Filippi and Barbara Motyl. Large Language Models (LLMs) in Engineering Education: A Systematic Review and Suggestions for Practical Adoption. Information, 2024."
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   "label": "Recoverable",
   "source": {
    "title": "A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions",
    "authors": [
     "Lei Huang",
     "Weijiang Yu",
     "Weitao Ma",
     "Weihong Zhong",
     "Zhangyin Feng",
     "Haotian Wang",
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     "Weihua Peng",
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     "Bing Qin",
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    ],
    "venue": "ACM Transactions on Information Systems",
    "year": 2024
   },
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  },
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   "id": 50,
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   "label": "Unrecoverable",
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   "label": "Recoverable",
   "source": {
    "title": "INTransformer: Data augmentation-based contrastive learning by injecting noise into transformer for molecular property prediction",
    "authors": [
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     "Ruisheng Zhang",
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    "year": 2024
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   "label": "Recoverable",
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    "title": "Smiles-bert: large scale unsupervised pre-training for molecular property prediction",
    "authors": [
     "Sheng Wang",
     "Yuzhi Guo",
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    "venue": "Proceedings of the 10th ACM international conference on bioinformatics, computational biology and health informatics",
    "year": 2019
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   "label": "Recoverable",
   "source": {
    "title": "ReactionT5: a large-scale pre-trained model towards application of limited reaction data",
    "authors": [
     "Tatsuya Sagawa",
     "Ryosuke Kojima"
    ],
    "venue": "arXiv (Cornell University)",
    "year": 2023
   },
   "corrected_citation": "Tatsuya Sagawa and Ryosuke Kojima. ReactionT5: a large-scale pre-trained model towards application of limited reaction data. arXiv preprint arXiv:2311.06708, 2023."
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   "citation": "Xinyu Fei, Boxin Wang, Jiaqi Hu, Rujing Xu, Wei Zhang, Bin Cao, Xuemin Liu, Jiaxin Liu, Junchi Tang, Zhiyuan Lin, et al. Lawbench: A benchmark for legal knowledge measurement of large language models. arXiv preprint arXiv:2309.16289, 2023.",
   "label": "Recoverable",
   "source": {
    "title": "Lawbench: Benchmarking legal knowledge of large language models",
    "authors": [
     "Zhiwei Fei",
     "Xiaoyu Shen",
     "Dawei Zhu",
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     "Alan Huang",
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     "others"
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    "year": 2024
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   "source": {
    "title": "Unveiling the power of language models in chemical research question answering",
    "authors": [
     "Xiuying Chen",
     "Tairan Wang",
     "Taicheng Guo",
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     "Haoyang Li",
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     "Xiangliang Zhang"
    ],
    "venue": "Communications Chemistry",
    "year": 2025
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    "title": "COVERAGE — A novel database for copy-move forgery detection",
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    "title": "CASIA Image Tampering Detection Evaluation Database",
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     "Jing Dong",
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    "venue": "2013 IEEE China Summit and International Conference on Signal and Information Processing",
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   "source": {
    "title": "TruFor: Leveraging All-Round Clues for Trustworthy Image Forgery Detection and Localization",
    "authors": [
     "Fabrizio Guillaro",
     "Davide Cozzolino",
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    "venue": "2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
    "year": 2023
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   "label": "Recoverable",
   "source": {
    "title": "CAT-Net: Compression Artifact Tracing Network for Detection and Localization of Image Splicing",
    "authors": [
     "Myung-Joon Kwon",
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    "venue": "2021 IEEE Winter Conference on Applications of Computer Vision (WACV)",
    "year": 2021
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   "label": "Recoverable",
   "source": {
    "title": "LISA: Reasoning Segmentation via Large Language Model",
    "authors": [
     "Xin Lai",
     "Zhuotao Tian",
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    "venue": "2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
    "year": 2024
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   "label": "Recoverable",
   "source": {
    "title": null,
    "authors": [],
    "venue": null,
    "year": null,
    "url": "https://www.kaggle.com/datasets/qsii24/tampcoco"
   },
   "corrected_citation": "Myung-Joon Kwon et al. CAT-Net tampCOCO: Image Splicing Dataset. Kaggle, 2021b. https://www.kaggle.com/datasets/qsii24/tampcoco."
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   "label": "Recoverable",
   "source": {
    "title": "IMD2020: A Large-Scale Annotated Dataset Tailored for Detecting Manipulated Images",
    "authors": [
     "Adam Novozámský",
     "Babak Mahdian",
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    ],
    "venue": "2020 IEEE Winter Applications of Computer Vision Workshops (WACVW)",
    "year": 2020
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   "label": "Recoverable",
   "source": {
    "title": "ObjectFormer for Image Manipulation Detection and Localization",
    "authors": [
     "Junke Wang",
     "Zuxuan Wu",
     "Jingjing Chen",
     "Xintong Han",
     "Abhinav Shrivastava",
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    ],
    "venue": "2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)",
    "year": 2022
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   "corrected_citation": "Junke Wang, Zuxuan Wu, Jingjing Chen, Xintong Han, Abhinav Shrivastava, Ser-Nam Lim, and Yu–Gang Jiang. ObjectFormer for Image Manipulation Detection and Localization. 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022."
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   "label": "Recoverable",
   "source": {
    "title": "Segment Everything Everywhere All at Once",
    "authors": [
     "Xueyan Zou",
     "Jianwei Yang",
     "Hao Zhang",
     "Feng Li",
     "Linjie Li",
     "Jianfeng Wang",
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    "venue": "Advances in Neural Information Processing Systems 36",
    "year": 2023
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   "label": "Recoverable",
   "source": {
    "title": "Aigi-Holmes: Towards Explainable and Generalizable AI-Generated Image Detection via Multimodal Large Language Models",
    "authors": [
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    "venue": "2025 IEEE/CVF International Conference on Computer Vision (ICCV)",
    "year": 2025
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   "label": "Recoverable",
   "source": {
    "title": "Language Models are Few-Shot Learners",
    "authors": [
     "Tom B. Brown",
     "Benjamin Mann",
     "Nick Ryder",
     "Melanie Subbiah",
     "Jared Kaplan",
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    "venue": "Neural Information Processing Systems",
    "year": 2020
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   "corrected_citation": "Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei. Language Models are Few-Shot Learners. Neural Information Processing Systems, 2020."
  },
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   "id": 70,
   "citation": "Gabriele Cicero et al. Nemotron-4 340B Technical Report. Technical report, NVIDIA, 2024.",
   "label": "Recoverable",
   "source": {
    "title": "Nemotron-4 340B Technical Report",
    "authors": [
     "Nvidia",
     "Bo Adler",
     "Niket Agarwal",
     "Ashwath Aithal",
     "Dong H. Anh",
     "Pallab Bhattacharya",
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    ],
    "venue": "arXiv",
    "year": 2024
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   "corrected_citation": "Nvidia, Bo Adler, Niket Agarwal, Ashwath Aithal, Dong H. Anh, Pallab Bhattacharya, Annika Brundyn, Jared Casper, Bryan Catanzaro, Sharon Clay, Jonathan Cohen, Sirshak Das, Ayush Dattagupta, Olivier Delalleau, Leon Derczynski, Yi Dong, Daniel Egert, Ellie Evans, Aleksander Ficek, Denys Fridman, Shaona Ghosh, Boris Ginsburg, Igor Gitman, Tomasz Grzegorzek, Robert Hero, Jining Huang, Vibhu Jawa, Joseph Jennings, Aastha Jhunjhunwala, John Kamalu, Sadaf Khan, Oleksii Kuchaiev, Patrick LeGresley, Hui Li, Jiwei Liu, Zihan Liu, Eileen Long, Ameya Sunil Mahabaleshwarkar, Somshubra Majumdar, James Maki, Miguel Martinez, Maer Rodrigues de Melo, Ivan Moshkov, Deepak Narayanan, Sean Narenthiran, Jesus Navarro, Phong Nguyen, Osvald Nitski, Vahid Noroozi, Guruprasad Nutheti, Christopher Parisien, Jupinder Parmar, Mostofa Patwary, Krzysztof Pawelec, Wei Ping, Shrimai Prabhumoye, Rajarshi Roy, Trisha Saar, Vasanth Rao Naik Sabavat, Sanjeev Satheesh, Jane Polak Scowcroft, Jason Sewall, Pavel Shamis, Gerald Shen, Mohammad Shoeybi, Dave Sizer, Misha Smelyanskiy, Felipe Soares, Makesh Narsimhan Sreedhar, Dan Su, Sandeep Subramanian, Shengyang Sun, Shubham Toshniwal, Hao Wang, Zhilin Wang, Jiaxuan You, Jiaqi Zeng, Jimmy Zhang, Jing Zhang, Vivienne Zhang, Yian Zhang, and Chen Zhu. Nemotron-4 340B Technical Report. arXiv preprint arXiv:2406.11704, 2024."
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   "label": "Recoverable",
   "source": {
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    "authors": [
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    "authors": [
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   "citation": "Ariel Lee. Open-platypus. https://huggingface.co/datasets/garage-bAInd/ Open-Platypus, 2023.",
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    "url": "https://huggingface.co/datasets/nvidia/Nemotron-PrismMath"
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    "authors": [
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    "authors": [
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    "url": "https://modelscope.cn/datasets/AI-ModelScope/CodeAlpaca-20k"
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   "citation": "Art B Owen. Monte Carlo theory and practice. Unpublished, 2013.",
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   "source": {
    "title": "Monte Carlo theory and practice",
    "authors": [
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    "venue": "Reports on Progress in Physics",
    "year": 1980
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     "David Rein",
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   "citation": "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, pp. 1-4. ACM, 2018.",
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    "title": "Remaining Useful Life Prediction Based on Deep Learning: A Survey",
    "authors": [
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    "venue": "arXiv",
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