@inproceedings{sabzevari-etal-2025-nlpart,
title = "{NLPART} at {S}em{E}val-2025 Task 4: Forgetting is harder than Learning",
author = "Sabzevari, Hoorieh and
Molazadeh Oskuee, Milad and
Abedini, Tohid and
Zamaninejad, Ghazal and
Baruni, Sara and
Amirmahani, Zahra and
Salehoof, Amirmohammad",
editor = "Rosenthal, Sara and
Ros{\'a}, Aiala and
Ghosh, Debanjan and
Zampieri, Marcos",
booktitle = "Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.semeval-1.304/",
pages = "2336--2341",
ISBN = "979-8-89176-273-2",
abstract = "Unlearning is a critical capability for ensuring privacy, security, and compliance in AI systems, enabling models to forget specific data while retaining overall performance. In this work, we participated in Task 4 of SemEval 2025, which focused on unlearning across three sub-tasks: (1) long-form synthetic creative documents, (2) short-form synthetic biographies containing personally identifiable information, and (3) real documents sampled from the target model{'}s training dataset. We conducted four experiments, employing Supervised Fine-Tuning (SFT) and Negative Preference Optimization (NPO). Despite achieving good performance on the retain set{---}data that the model was supposed to remember{---}our findings demonstrate that these techniques did not perform well on the forget set, where unlearning was required."
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<abstract>Unlearning is a critical capability for ensuring privacy, security, and compliance in AI systems, enabling models to forget specific data while retaining overall performance. In this work, we participated in Task 4 of SemEval 2025, which focused on unlearning across three sub-tasks: (1) long-form synthetic creative documents, (2) short-form synthetic biographies containing personally identifiable information, and (3) real documents sampled from the target model’s training dataset. We conducted four experiments, employing Supervised Fine-Tuning (SFT) and Negative Preference Optimization (NPO). Despite achieving good performance on the retain set—data that the model was supposed to remember—our findings demonstrate that these techniques did not perform well on the forget set, where unlearning was required.</abstract>
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%0 Conference Proceedings
%T NLPART at SemEval-2025 Task 4: Forgetting is harder than Learning
%A Sabzevari, Hoorieh
%A Molazadeh Oskuee, Milad
%A Abedini, Tohid
%A Zamaninejad, Ghazal
%A Baruni, Sara
%A Amirmahani, Zahra
%A Salehoof, Amirmohammad
%Y Rosenthal, Sara
%Y Rosá, Aiala
%Y Ghosh, Debanjan
%Y Zampieri, Marcos
%S Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-273-2
%F sabzevari-etal-2025-nlpart
%X Unlearning is a critical capability for ensuring privacy, security, and compliance in AI systems, enabling models to forget specific data while retaining overall performance. In this work, we participated in Task 4 of SemEval 2025, which focused on unlearning across three sub-tasks: (1) long-form synthetic creative documents, (2) short-form synthetic biographies containing personally identifiable information, and (3) real documents sampled from the target model’s training dataset. We conducted four experiments, employing Supervised Fine-Tuning (SFT) and Negative Preference Optimization (NPO). Despite achieving good performance on the retain set—data that the model was supposed to remember—our findings demonstrate that these techniques did not perform well on the forget set, where unlearning was required.
%U https://aclanthology.org/2025.semeval-1.304/
%P 2336-2341
Markdown (Informal)
[NLPART at SemEval-2025 Task 4: Forgetting is harder than Learning](https://aclanthology.org/2025.semeval-1.304/) (Sabzevari et al., SemEval 2025)
ACL
- Hoorieh Sabzevari, Milad Molazadeh Oskuee, Tohid Abedini, Ghazal Zamaninejad, Sara Baruni, Zahra Amirmahani, and Amirmohammad Salehoof. 2025. NLPART at SemEval-2025 Task 4: Forgetting is harder than Learning. In Proceedings of the 19th International Workshop on Semantic Evaluation (SemEval-2025), pages 2336–2341, Vienna, Austria. Association for Computational Linguistics.