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.

We present MeSSI, a multi-module system applied to SemEval 2025’s task 3: Mu-SHROOM. Our system tags questions in order to obtain semantic relevant terms that are used as information retrieval characteristics. Said characteristics serve as extraction terms for Wikipedia pages that are in turn processed to generate gold standard texts used in a hallucination evaluation system. A PoST-based entity comparison was implemented to contrast the test dataset sentences with the corresponding generated gold standards, wich in turn was the main criteria to tag hallucinations, partitioned in soft labels and hard labels. This method was tested in Spanish and English, finishing 18th and 19th respectively on the IoU based ranking.

This paper explores whether it is possible to train a machine learning model using Wikipedia data to detect subjectivity in sentences and generalize effectively to other domains. To achieve this, we performed experiments with the WikiBias corpus, the BABE corpus, and the CheckThat! Dataset. Various classical models for ML were tested, including Logistic Regression, SVC, and SVR, including characteristics such as Sentence Transformers similarity, probabilistic sentiment measures, and biased lexicons. Pre-trained models like DistilRoBERTa, as well as large language models like Gemma and GPT-4, were also tested for the same classification task.