@inproceedings{salas-jimenez-etal-2025-gil,
title = "{GIL}-{IIMAS} {UNAM} at {S}em{E}val-2025 Task 4: {LA}-Min({E}): {LLM} Unlearning Approaches Under Function Minimizing Evaluation Constraints",
author = "Salas - Jimenez, Karla and
L{\'o}pez - Ponce, Francisco and
Hern{\'a}ndez - Bustamante, Diego and
Bel - Enguix, Gemma and
G{\'o}mez - Adorno, Helena",
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-1.205/",
pages = "1557--1562",
ISBN = "979-8-89176-273-2",
abstract = "This paper describes Gradient Ascent and Task Vectors as LLM unlearning methodologies applied to SemEval 2025{'}s task 4. This task focuses on LLM unlearning on specific information under the constraints of preserving the model{'}s advanced text generation capabilities; meaning that our implementations of these algorithms were constrained both in the information datasets as well as the overall effect of each algorithm in the model{'}s general performance. Our implementation produced modified language models that ranked 7th out of 14 valid participants in the 7B parameter model, and 6th out of 24 in the 1B parameter model."
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<abstract>This paper describes Gradient Ascent and Task Vectors as LLM unlearning methodologies applied to SemEval 2025’s task 4. This task focuses on LLM unlearning on specific information under the constraints of preserving the model’s advanced text generation capabilities; meaning that our implementations of these algorithms were constrained both in the information datasets as well as the overall effect of each algorithm in the model’s general performance. Our implementation produced modified language models that ranked 7th out of 14 valid participants in the 7B parameter model, and 6th out of 24 in the 1B parameter model.</abstract>
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%0 Conference Proceedings
%T GIL-IIMAS UNAM at SemEval-2025 Task 4: LA-Min(E): LLM Unlearning Approaches Under Function Minimizing Evaluation Constraints
%A Salas - Jimenez, Karla
%A López - Ponce, Francisco
%A Hernández - Bustamante, Diego
%A Bel - Enguix, Gemma
%A Gómez - Adorno, Helena
%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 salas-jimenez-etal-2025-gil
%X This paper describes Gradient Ascent and Task Vectors as LLM unlearning methodologies applied to SemEval 2025’s task 4. This task focuses on LLM unlearning on specific information under the constraints of preserving the model’s advanced text generation capabilities; meaning that our implementations of these algorithms were constrained both in the information datasets as well as the overall effect of each algorithm in the model’s general performance. Our implementation produced modified language models that ranked 7th out of 14 valid participants in the 7B parameter model, and 6th out of 24 in the 1B parameter model.
%U https://aclanthology.org/2025.semeval-1.205/
%P 1557-1562
Markdown (Informal)
[GIL-IIMAS UNAM at SemEval-2025 Task 4: LA-Min(E): LLM Unlearning Approaches Under Function Minimizing Evaluation Constraints](https://aclanthology.org/2025.semeval-1.205/) (Salas - Jimenez et al., SemEval 2025)
ACL