  With the increasing influence of social media platforms, it has become
crucial to develop automated systems capable of detecting instances of sexism
and other disrespectful and hateful behaviors to promote a more inclusive and
respectful online environment. Nevertheless, these tasks are considerably
challenging considering different hate categories and the author's intentions,
especially under the learning with disagreements regime. This paper describes
AI-UPV team's participation in the EXIST (sEXism Identification in Social
neTworks) Lab at CLEF 2023. The proposed approach aims at addressing the task
of sexism identification and characterization under the learning with
disagreements paradigm by training directly from the data with disagreements,
without using any aggregated label. Yet, performances considering both soft and
hard evaluations are reported. The proposed system uses large language models
(i.e., mBERT and XLM-RoBERTa) and ensemble strategies for sexism identification
and classification in English and Spanish. In particular, our system is
articulated in three different pipelines. The ensemble approach outperformed
the individual large language models obtaining the best performances both
adopting a soft and a hard label evaluation. This work describes the
participation in all the three EXIST tasks, considering a soft evaluation, it
obtained fourth place in Task 2 at EXIST and first place in Task 3, with the
highest ICM-Soft of -2.32 and a normalized ICM-Soft of 0.79. The source code of
our approaches is publicly available at
https://github.com/AngelFelipeMP/Sexism-LLM-Learning-With-Disagreement.
