Enhancing the De-identification of Personally Identifiable Information in Educational Data
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Published
September 13, 2025
Zilyu Ji
Yuntian Shen
Kenneth R. Koedinger
Jionghao Lin
https://orcid.org/0009-0002-0842-7984
Abstract
Protecting Personally Identifiable Information (PII), such as names, is a critical requirement in educational data mining to safeguard the privacy of students and teachers and maintain trust. Accurate PII detection is an essential step toward anonymizing sensitive information while preserving the utility of educational data. Motivated by recent advancements in artificial intelligence, our study investigates the GPT-4o-mini model as a cost-effective and efficient solution for PII detection tasks. We explore both prompting and fine-tuning approaches and compare GPT-4o-mini’s performance against established frameworks, including Microsoft Presidio and Azure AI Language. Our evaluation on two public datasets, CRAPII and TSCC, demonstrates that the fine-tuned GPT-4o-mini model achieves superior performance, with a recall of 0.9589 on CRAPII. Additionally, fine-tuned GPT-4o-mini significantly improves precision scores (a threefold increase) while reducing computational costs to nearly one-tenth of those associated with Azure AI Language. Furthermore, our bias analysis reveals that the fine-tuned GPT-4o-mini model consistently delivers accurate results across diverse cultural backgrounds and genders. The generalizability analysis using the TSCC dataset further highlights its robustness, achieving a recall of 0.9895 with minimal additional training data from TSCC. These results emphasize the potential of fine-tuned GPT-4o-mini as an accurate and cost-effective tool for PII detection in educational data. It offers robust privacy protection while preserving the data’s utility for research and pedagogical analysis. Our code is available on GitHub: https://github.com/AnonJD/PrivacyAI
How to Cite
Enhancing the De-identification of Personally Identifiable Information in Educational Data. (2025). Journal of Educational Data Mining, 17(2), 55-85. https://doi.org/10.5281/zenodo.17114271
Details
Keywords
privacy, de-identification, personally identifiable information, large language models, fine-tuning GPT, cost-effectiveness, hidden in plain sight
References
Ball, T., Chen, S., and Herley, C. 2024. Can we count on LLMs? the fixed-effect fallacy and claims of GPT-4 capabilities. Transactions on Machine Learning Research.
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S. 2021. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’21. Association for Computing Machinery, New York, NY, USA, 610–623.
Brandfonbrener, D., Henniger, S., Raja, S., Prasad, T., Loughridge, C. R., Cassano, F., Hu, S. R., Yang, J., Byrd, W. E., Zinkov, R., and Amin, N. 2024. VerMCTS: Synthesizing multi-step programs using a verifier, a large language model, and tree search. In The 4th Workshop on Mathematical Reasoning and AI at NeurIPS’24. The 4th Workshop on Mathematical Reasoning and AI, Online.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. 2020. Language models are few-shot learners.
Caines, A., Yannakoudakis, H., Allen, H., Pérez-Paredes, P., Byrne, B., and Buttery, P. 2022. The teacher-student chatroom corpus version 2: More lessons, new annotation, automatic detection of sequence shifts. In Proceedings of the 11th Workshop on NLP for Computer Assisted Language Learning, D. Alfter, E. Volodina, T. François, P. Desmet, F. Cornillie, A. Jönsson, and E. Rennes, Eds. LiU Electronic Press, Louvain-la-Neuve, Belgium, 23–35.
Carmichael, L., Poirier, S.-M., Coursaris, C. K., Léger, P.-M., and Sénécal, S. 2022. Users’ information disclosure behaviors during interactions with chatbots: the effect of information disclosure nudges. Applied Sciences 12, 24, 12660.
Carrell, D., Malin, B., Aberdeen, J., Bayer, S., Clark, C., Wellner, B., and Hirschman, L. 2012. Hiding in plain sight: Use of realistic surrogates to reduce exposure of protected health information in clinical text. Journal of the American Medical Informatics Association 20, 2 (07), 342–348.
Carrell, D. S., Cronkite, D. J., Li, M. R., Nyemba, S., Malin, B. A., Aberdeen, J. S., and Hirschman, L. 2019. The machine giveth and the machine taketh away: A parrot attack on clinical text deidentified with hiding in plain sight. Journal of the American Medical Informatics Association 26, 12 (08), 1536–1544.
Carrell, D. S., Malin, B. A., Cronkite, D. J., Aberdeen, J. S., Clark, C., Li, M. R., Bastakoty, D., Nyemba, S., and Hirschman, L. 2020. Resilience of clinical text de-identified with “hiding in plain sight” to hostile reidentification attacks by human readers. J. Am. Med. Inform. Assoc. 27, 9 (Jul), 1374–1382.
Carvalho, T., Moniz, N., Faria, P., and Antunes, L. 2022. Survey on privacy-preserving techniques for data publishing.
Chen, J., Fife, J. H., Bejar, I. I., and Rupp, A. A. 2016. Building e-rater® scoring models using machine learning methods. ETS Research Report Series 2016, 1, 1–12.
Fung, B. C. M., Wang, K., Chen, R., and Yu, P. S. 2010. Privacy-preserving data publishing: A survey of recent developments. ACM Computing Surveys (CSUR) 42, 4 (June), 1–53.
Gadre, S. Y., Smyrnis, G., Shankar, V., Gururangan, S., Wortsman, M., Shao, R., Mercat, J., Fang, A., Li, J., Keh, S., Xin, R., Nezhurina, M., Vasiljevic, I., Soldaini, L., Jitsev, J., Dimakis, A., Ilharco, G., Koh, P. W., Song, S., Kollar, T., Carmon, Y., Dave, A., Heckel, R., Muennighoff, N., and Schmidt, L. 2025. Language models scale reliably with over-training and on downstream tasks. In The Thirteenth International Conference on Learning Representations. International Conference on Learning Representations, Singapore.
Grattafiori, A. et al. 2024. The Llama 3 herd of models.
Hardt, M., Price, E., Price, E., and Srebro, N. 2016. Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds. Vol. 29. Curran Associates, Inc., Barcelona, Spain, 3315–3323.
Hofstede, G. 2001. Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Sage Publications, Thousand Oaks, CA.
Holmes, L., Crossley, S., Hayes, N., Kuehl, D., Trumbore, A., and Gutu-Robu, G. 2023. De-identification of student writing in technologically mediated educational settings. In Polyphonic Construction of Smart Learning Ecosystems, M. Dascalu, P. Marti, and F. Pozzi, Eds. Springer Nature Singapore, Singapore, 177–189.
Holmes, L., Crossley, S., Wang, J., and Zhang, W. 2024. The cleaned repository of annotated personally identifiable information. In Proceedings of the 17th International Conference on Educational Data Mining, B. Paaßen and C. D. Epp, Eds. International Educational Data Mining Society, Atlanta, Georgia, USA, 790–796.
Ive, J., Yadav, V., Ignashina, M., Rand, M., and Bondaronek, P. 2024. Privacy-preserving behaviour of chatbot users: Steering through trust dynamics.
Jackson, J. C. and Medvedev, D. 2024. Worldwide divergence of values. Nature Communications 15, 1, 2650.
Jegham, N., Abdelatti, M., Elmoubarki, L., and Hendawi, A. 2025. How hungry is AI? Benchmarking energy, water, and carbon footprint of LLM inference.
Lee, S., Seo, S., Kim, J., Lee, Y., and Hwang, S. 2023. Few-shot fine-tuning is all you need for source-free domain adaptation.
Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., and Chen, W. 2023. Making language models better reasoners with step-aware verifier. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), A. Rogers, J. Boyd-Graber, and N. Okazaki, Eds. Association for Computational Linguistics, Toronto, Canada, 5315–5333.
Lin, J., Chen, E., Han, Z., Gurung, A., Thomas, D. R., Tan, W., Nguyen, N. D., and Koedinger, K. R. 2024. How can I improve? Using GPT to highlight the desired and undesired parts of open-ended responses. In Proceedings of the 17th International Conference on Educational Data Mining, B. Paaßen and C. D. Epp, Eds. International Educational Data Mining Society, Atlanta, Georgia, USA, 236–250.
Lison, P., Pilán, I., Sanchez, D., Batet, M., and Øvrelid, L. 2021. Anonymisation models for text data: State of the art, challenges and future directions. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Association for Computational Linguistics, Online, 4188–4203.
Liu, Y., Iter, D., Xu, Y., Wang, S., Xu, R., and Zhu, C. 2023. G-Eval: NLG evaluation using GPT-4 with better human alignment. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, Singapore, 2511–2522.
Mansfield, C., Paullada, A., and Howell, K. 2022. Behind the mask: Demographic bias in name detection for PII masking. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, B. R. Chakravarthi, B. Bharathi, J. P. McCrae, M. Zarrouk, K. Bali, and P. Buitelaar, Eds. Association for Computational Linguistics, Dublin, Ireland, 76–89.
Mosbach, M., Pimentel, T., Ravfogel, S., Klakow, D., and Elazar, Y. 2023. Few-shot fine-tuning vs. in-context learning: A fair comparison and evaluation. In Findings of the Association for Computational Linguistics: ACL 2023, A. Rogers, J. Boyd-Graber, and N. Okazaki, Eds. Association for Computational Linguistics, Toronto, Canada, 12284–12314.
Nasr, M., Rando, J., Carlini, N., Hayase, J., Jagielski, M., Cooper, A. F., Ippolito, D., Choquette-Choo, C. A., Tramèr, F., and Lee, K. 2025. Scalable extraction of training data from aligned, production language models. In The Thirteenth International Conference on Learning Representations. International Conference on Learning Representations, Singapore.
Osborne, J. D., O’Leary, T., Nadimpalli, A., Aly, S. M., and Kennedy, R. E. 2022. Bratsynthetic: Text de-identification using a markov chain replacement strategy for surrogate personal identifying information. CoRR abs/2210.16125, 1–18.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., and Lowe, R. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds. Vol. 35. Curran Associates, Inc., New Orleans, LA, USA, 27730–27744.
Pal, A., Bhargava, R., Hinsz, K., Esterhuizen, J., and Bhattacharya, S. 2024. The empirical impact of data sanitization on language models. In Neurips Safe Generative AI Workshop 2024. Safe Generative AI Workshop, Vancouver, Canada.
Portnoff, L., Gustafson, E., Rollinson, J., and Bicknell, K. 2021. Methods for language learning assessment at scale: Dulingo case study. In Proceedings of The 14th International Conference on Educational Data Mining (EDM21). International Educational Data Mining Society, International Educational Data Mining Society, Paris, France, 865–871.
Reidenberg, J. R. and Schaub, F. 2018. Achieving big data privacy in education. Theory and Research in Education 16, 3, 263–279.
Samarati, P. 2001. Protecting respondents identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13, 6, 1010–1027.
Samsi, S., Zhao, D., McDonald, J., Li, B., Michaleas, A., Jones, M., Bergeron, W., Kepner, J., Tiwari, D., and Gadepally, V. 2023. From words to watts: Benchmarking the energy costs of large language model inference. In 2023 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, Boston, MA, USA, 1–9.
Sang, E. F. T. K. and Meulder, F. D. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003. NAACL, Association for Computational Linguistics, Edmonton, Canada, 142–147.
Shen, Z., Liu, Z., Qin, J., Savvides, M., and Cheng, K.-T. 2021. Partial is better than all: Revisiting fine-tuning strategy for few-shot learning. Proceedings of the AAAI Conference on Artificial Intelligence 35, 11 (May), 9594–9602.
Singh, A., Pandey, N., Shirgaonkar, A., Manoj, P., and Aski, V. 2024. A study of optimizations for fine-tuning large language models.
Singhal, S., Zambrano, A. F., Pankiewicz, M., Liu, X., Porter, C., and Baker, R. S. 2024. De-identifying student personally identifying information with GPT-4. In Proceedings of the 17th International Conference on Educational Data Mining. International Educational Data Mining Society, Atlanta, Georgia, USA, 559–565.
Sun, F.-K., Ho, C.-H., and Lee, H.-Y. 2020. LAMOL: LAnguage MOdeling for lifelong language learning. In International Conference on Learning Representations. International Conference on Learning Representations, Online.
Sánchez, D. and Batet, M. 2015. C-sanitized: A privacy model for document redaction and sanitization. Journal of the Association for Information Science and Technology 67, 1 (Apr.), 148–163.
Tirumala, K., Markosyan, A., Zettlemoyer, L., and Aghajanyan, A. 2022. Memorization without overfitting: Analyzing the training dynamics of large language models. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds. Vol. 35. Curran Associates, Inc., New Orleans LA USA, 38274–38290.
Wang, F., Wan, X., Sun, R., Chen, J., and Arik, S. O. 2025. Astute RAG: Overcoming imperfect retrieval augmentation and knowledge conflicts for large language models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), W. Che, J. Nabende, E. Shutova, and M. T. Pilehvar, Eds. Association for Computational Linguistics, Vienna, Austria, 30553–30571.
Wang, S., Sun, X., Li, X., Ouyang, R., Wu, F., Zhang, T., Li, J., Wang, G., and Guo, C. 2025. GPT-NER: Named entity recognition via large language models. In Findings of the Association for Computational Linguistics: NAACL 2025, L. Chiruzzo, A. Ritter, and L. Wang, Eds. Association for Computational Linguistics, Albuquerque, New Mexico, 4257–4275.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., ichter, b., Xia, F., Chi, E., Le, Q. V., and Zhou, D. 2022. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds. Vol. 35. Curran Associates, Inc., New Orleans, LA, USA, 24824–24837.
Zambrano, A. F., Singhal, S., Pankiewicz, M., Baker, R. S., Porter, C., and Liu, X. 2025. De-identifying student personally identifying information in discussion forum posts with large language models. Information and Learning Science. 126, 5/6, 401–424.
Zeide, E. 2018. Education technology and student privacy. In The Cambridge Handbook of Consumer Privacy, E. Selinger, J. Polonetsky, and O. Tene, Eds. The MIT Press, Cambridge, MA, 70–84.
Zhang, X., Rajabi, N., Duh, K., and Koehn, P. 2023. Machine translation with large language models: Prompting, few-shot learning, and fine-tuning with QLoRA. In Proceedings of the Eighth Conference on Machine Translation. Association for Computational Linguistics, Association for Computational Linguistics, Singapore, 468–481.
Zhao, J., Wang, S., Zhao, Y., Hou, X., Wang, K., Gao, P., Zhang, Y., Wei, C., and Wang, H. 2024. Models are codes: Towards measuring malicious code poisoning attacks on pre-trained model hubs. In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering. ASE ’24. ACM, Sacramento CA, USA, 2087–2098.
Zhao, X., Niazi, A., and Rios, A. 2024. A comprehensive study of gender bias in chemical named entity recognition models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), K. Duh, H. Gomez, and S. Bethard, Eds. Association for Computational Linguistics, Mexico City, Mexico, 4360–4374.
Bender, E. M., Gebru, T., McMillan-Major, A., and Shmitchell, S. 2021. On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency. FAccT ’21. Association for Computing Machinery, New York, NY, USA, 610–623.
Brandfonbrener, D., Henniger, S., Raja, S., Prasad, T., Loughridge, C. R., Cassano, F., Hu, S. R., Yang, J., Byrd, W. E., Zinkov, R., and Amin, N. 2024. VerMCTS: Synthesizing multi-step programs using a verifier, a large language model, and tree search. In The 4th Workshop on Mathematical Reasoning and AI at NeurIPS’24. The 4th Workshop on Mathematical Reasoning and AI, Online.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D., Wu, J., Winter, C., Hesse, C., Chen, M., Sigler, E., Litwin, M., Gray, S., Chess, B., Clark, J., Berner, C., McCandlish, S., Radford, A., Sutskever, I., and Amodei, D. 2020. Language models are few-shot learners.
Caines, A., Yannakoudakis, H., Allen, H., Pérez-Paredes, P., Byrne, B., and Buttery, P. 2022. The teacher-student chatroom corpus version 2: More lessons, new annotation, automatic detection of sequence shifts. In Proceedings of the 11th Workshop on NLP for Computer Assisted Language Learning, D. Alfter, E. Volodina, T. François, P. Desmet, F. Cornillie, A. Jönsson, and E. Rennes, Eds. LiU Electronic Press, Louvain-la-Neuve, Belgium, 23–35.
Carmichael, L., Poirier, S.-M., Coursaris, C. K., Léger, P.-M., and Sénécal, S. 2022. Users’ information disclosure behaviors during interactions with chatbots: the effect of information disclosure nudges. Applied Sciences 12, 24, 12660.
Carrell, D., Malin, B., Aberdeen, J., Bayer, S., Clark, C., Wellner, B., and Hirschman, L. 2012. Hiding in plain sight: Use of realistic surrogates to reduce exposure of protected health information in clinical text. Journal of the American Medical Informatics Association 20, 2 (07), 342–348.
Carrell, D. S., Cronkite, D. J., Li, M. R., Nyemba, S., Malin, B. A., Aberdeen, J. S., and Hirschman, L. 2019. The machine giveth and the machine taketh away: A parrot attack on clinical text deidentified with hiding in plain sight. Journal of the American Medical Informatics Association 26, 12 (08), 1536–1544.
Carrell, D. S., Malin, B. A., Cronkite, D. J., Aberdeen, J. S., Clark, C., Li, M. R., Bastakoty, D., Nyemba, S., and Hirschman, L. 2020. Resilience of clinical text de-identified with “hiding in plain sight” to hostile reidentification attacks by human readers. J. Am. Med. Inform. Assoc. 27, 9 (Jul), 1374–1382.
Carvalho, T., Moniz, N., Faria, P., and Antunes, L. 2022. Survey on privacy-preserving techniques for data publishing.
Chen, J., Fife, J. H., Bejar, I. I., and Rupp, A. A. 2016. Building e-rater® scoring models using machine learning methods. ETS Research Report Series 2016, 1, 1–12.
Fung, B. C. M., Wang, K., Chen, R., and Yu, P. S. 2010. Privacy-preserving data publishing: A survey of recent developments. ACM Computing Surveys (CSUR) 42, 4 (June), 1–53.
Gadre, S. Y., Smyrnis, G., Shankar, V., Gururangan, S., Wortsman, M., Shao, R., Mercat, J., Fang, A., Li, J., Keh, S., Xin, R., Nezhurina, M., Vasiljevic, I., Soldaini, L., Jitsev, J., Dimakis, A., Ilharco, G., Koh, P. W., Song, S., Kollar, T., Carmon, Y., Dave, A., Heckel, R., Muennighoff, N., and Schmidt, L. 2025. Language models scale reliably with over-training and on downstream tasks. In The Thirteenth International Conference on Learning Representations. International Conference on Learning Representations, Singapore.
Grattafiori, A. et al. 2024. The Llama 3 herd of models.
Hardt, M., Price, E., Price, E., and Srebro, N. 2016. Equality of opportunity in supervised learning. In Advances in Neural Information Processing Systems, D. Lee, M. Sugiyama, U. Luxburg, I. Guyon, and R. Garnett, Eds. Vol. 29. Curran Associates, Inc., Barcelona, Spain, 3315–3323.
Hofstede, G. 2001. Culture’s consequences: Comparing values, behaviors, institutions and organizations across nations. Sage Publications, Thousand Oaks, CA.
Holmes, L., Crossley, S., Hayes, N., Kuehl, D., Trumbore, A., and Gutu-Robu, G. 2023. De-identification of student writing in technologically mediated educational settings. In Polyphonic Construction of Smart Learning Ecosystems, M. Dascalu, P. Marti, and F. Pozzi, Eds. Springer Nature Singapore, Singapore, 177–189.
Holmes, L., Crossley, S., Wang, J., and Zhang, W. 2024. The cleaned repository of annotated personally identifiable information. In Proceedings of the 17th International Conference on Educational Data Mining, B. Paaßen and C. D. Epp, Eds. International Educational Data Mining Society, Atlanta, Georgia, USA, 790–796.
Ive, J., Yadav, V., Ignashina, M., Rand, M., and Bondaronek, P. 2024. Privacy-preserving behaviour of chatbot users: Steering through trust dynamics.
Jackson, J. C. and Medvedev, D. 2024. Worldwide divergence of values. Nature Communications 15, 1, 2650.
Jegham, N., Abdelatti, M., Elmoubarki, L., and Hendawi, A. 2025. How hungry is AI? Benchmarking energy, water, and carbon footprint of LLM inference.
Lee, S., Seo, S., Kim, J., Lee, Y., and Hwang, S. 2023. Few-shot fine-tuning is all you need for source-free domain adaptation.
Li, Y., Lin, Z., Zhang, S., Fu, Q., Chen, B., Lou, J.-G., and Chen, W. 2023. Making language models better reasoners with step-aware verifier. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), A. Rogers, J. Boyd-Graber, and N. Okazaki, Eds. Association for Computational Linguistics, Toronto, Canada, 5315–5333.
Lin, J., Chen, E., Han, Z., Gurung, A., Thomas, D. R., Tan, W., Nguyen, N. D., and Koedinger, K. R. 2024. How can I improve? Using GPT to highlight the desired and undesired parts of open-ended responses. In Proceedings of the 17th International Conference on Educational Data Mining, B. Paaßen and C. D. Epp, Eds. International Educational Data Mining Society, Atlanta, Georgia, USA, 236–250.
Lison, P., Pilán, I., Sanchez, D., Batet, M., and Øvrelid, L. 2021. Anonymisation models for text data: State of the art, challenges and future directions. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, Association for Computational Linguistics, Online, 4188–4203.
Liu, Y., Iter, D., Xu, Y., Wang, S., Xu, R., and Zhu, C. 2023. G-Eval: NLG evaluation using GPT-4 with better human alignment. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, H. Bouamor, J. Pino, and K. Bali, Eds. Association for Computational Linguistics, Singapore, 2511–2522.
Mansfield, C., Paullada, A., and Howell, K. 2022. Behind the mask: Demographic bias in name detection for PII masking. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, B. R. Chakravarthi, B. Bharathi, J. P. McCrae, M. Zarrouk, K. Bali, and P. Buitelaar, Eds. Association for Computational Linguistics, Dublin, Ireland, 76–89.
Mosbach, M., Pimentel, T., Ravfogel, S., Klakow, D., and Elazar, Y. 2023. Few-shot fine-tuning vs. in-context learning: A fair comparison and evaluation. In Findings of the Association for Computational Linguistics: ACL 2023, A. Rogers, J. Boyd-Graber, and N. Okazaki, Eds. Association for Computational Linguistics, Toronto, Canada, 12284–12314.
Nasr, M., Rando, J., Carlini, N., Hayase, J., Jagielski, M., Cooper, A. F., Ippolito, D., Choquette-Choo, C. A., Tramèr, F., and Lee, K. 2025. Scalable extraction of training data from aligned, production language models. In The Thirteenth International Conference on Learning Representations. International Conference on Learning Representations, Singapore.
Osborne, J. D., O’Leary, T., Nadimpalli, A., Aly, S. M., and Kennedy, R. E. 2022. Bratsynthetic: Text de-identification using a markov chain replacement strategy for surrogate personal identifying information. CoRR abs/2210.16125, 1–18.
Ouyang, L., Wu, J., Jiang, X., Almeida, D., Wainwright, C., Mishkin, P., Zhang, C., Agarwal, S., Slama, K., Ray, A., Schulman, J., Hilton, J., Kelton, F., Miller, L., Simens, M., Askell, A., Welinder, P., Christiano, P. F., Leike, J., and Lowe, R. 2022. Training language models to follow instructions with human feedback. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds. Vol. 35. Curran Associates, Inc., New Orleans, LA, USA, 27730–27744.
Pal, A., Bhargava, R., Hinsz, K., Esterhuizen, J., and Bhattacharya, S. 2024. The empirical impact of data sanitization on language models. In Neurips Safe Generative AI Workshop 2024. Safe Generative AI Workshop, Vancouver, Canada.
Portnoff, L., Gustafson, E., Rollinson, J., and Bicknell, K. 2021. Methods for language learning assessment at scale: Dulingo case study. In Proceedings of The 14th International Conference on Educational Data Mining (EDM21). International Educational Data Mining Society, International Educational Data Mining Society, Paris, France, 865–871.
Reidenberg, J. R. and Schaub, F. 2018. Achieving big data privacy in education. Theory and Research in Education 16, 3, 263–279.
Samarati, P. 2001. Protecting respondents identities in microdata release. IEEE Transactions on Knowledge and Data Engineering 13, 6, 1010–1027.
Samsi, S., Zhao, D., McDonald, J., Li, B., Michaleas, A., Jones, M., Bergeron, W., Kepner, J., Tiwari, D., and Gadepally, V. 2023. From words to watts: Benchmarking the energy costs of large language model inference. In 2023 IEEE High Performance Extreme Computing Conference (HPEC). IEEE, Boston, MA, USA, 1–9.
Sang, E. F. T. K. and Meulder, F. D. 2003. Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003. NAACL, Association for Computational Linguistics, Edmonton, Canada, 142–147.
Shen, Z., Liu, Z., Qin, J., Savvides, M., and Cheng, K.-T. 2021. Partial is better than all: Revisiting fine-tuning strategy for few-shot learning. Proceedings of the AAAI Conference on Artificial Intelligence 35, 11 (May), 9594–9602.
Singh, A., Pandey, N., Shirgaonkar, A., Manoj, P., and Aski, V. 2024. A study of optimizations for fine-tuning large language models.
Singhal, S., Zambrano, A. F., Pankiewicz, M., Liu, X., Porter, C., and Baker, R. S. 2024. De-identifying student personally identifying information with GPT-4. In Proceedings of the 17th International Conference on Educational Data Mining. International Educational Data Mining Society, Atlanta, Georgia, USA, 559–565.
Sun, F.-K., Ho, C.-H., and Lee, H.-Y. 2020. LAMOL: LAnguage MOdeling for lifelong language learning. In International Conference on Learning Representations. International Conference on Learning Representations, Online.
Sánchez, D. and Batet, M. 2015. C-sanitized: A privacy model for document redaction and sanitization. Journal of the Association for Information Science and Technology 67, 1 (Apr.), 148–163.
Tirumala, K., Markosyan, A., Zettlemoyer, L., and Aghajanyan, A. 2022. Memorization without overfitting: Analyzing the training dynamics of large language models. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds. Vol. 35. Curran Associates, Inc., New Orleans LA USA, 38274–38290.
Wang, F., Wan, X., Sun, R., Chen, J., and Arik, S. O. 2025. Astute RAG: Overcoming imperfect retrieval augmentation and knowledge conflicts for large language models. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), W. Che, J. Nabende, E. Shutova, and M. T. Pilehvar, Eds. Association for Computational Linguistics, Vienna, Austria, 30553–30571.
Wang, S., Sun, X., Li, X., Ouyang, R., Wu, F., Zhang, T., Li, J., Wang, G., and Guo, C. 2025. GPT-NER: Named entity recognition via large language models. In Findings of the Association for Computational Linguistics: NAACL 2025, L. Chiruzzo, A. Ritter, and L. Wang, Eds. Association for Computational Linguistics, Albuquerque, New Mexico, 4257–4275.
Wei, J., Wang, X., Schuurmans, D., Bosma, M., ichter, b., Xia, F., Chi, E., Le, Q. V., and Zhou, D. 2022. Chain-of-thought prompting elicits reasoning in large language models. In Advances in Neural Information Processing Systems, S. Koyejo, S. Mohamed, A. Agarwal, D. Belgrave, K. Cho, and A. Oh, Eds. Vol. 35. Curran Associates, Inc., New Orleans, LA, USA, 24824–24837.
Zambrano, A. F., Singhal, S., Pankiewicz, M., Baker, R. S., Porter, C., and Liu, X. 2025. De-identifying student personally identifying information in discussion forum posts with large language models. Information and Learning Science. 126, 5/6, 401–424.
Zeide, E. 2018. Education technology and student privacy. In The Cambridge Handbook of Consumer Privacy, E. Selinger, J. Polonetsky, and O. Tene, Eds. The MIT Press, Cambridge, MA, 70–84.
Zhang, X., Rajabi, N., Duh, K., and Koehn, P. 2023. Machine translation with large language models: Prompting, few-shot learning, and fine-tuning with QLoRA. In Proceedings of the Eighth Conference on Machine Translation. Association for Computational Linguistics, Association for Computational Linguistics, Singapore, 468–481.
Zhao, J., Wang, S., Zhao, Y., Hou, X., Wang, K., Gao, P., Zhang, Y., Wei, C., and Wang, H. 2024. Models are codes: Towards measuring malicious code poisoning attacks on pre-trained model hubs. In Proceedings of the 39th IEEE/ACM International Conference on Automated Software Engineering. ASE ’24. ACM, Sacramento CA, USA, 2087–2098.
Zhao, X., Niazi, A., and Rios, A. 2024. A comprehensive study of gender bias in chemical named entity recognition models. In Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), K. Duh, H. Gomez, and S. Bethard, Eds. Association for Computational Linguistics, Mexico City, Mexico, 4360–4374.
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