  Sexism in online content is a pervasive issue that necessitates effective
classification techniques to mitigate its harmful impact. Online platforms
often have sexist comments and posts that create a hostile environment,
especially for women and minority groups. This content not only spreads harmful
stereotypes but also causes emotional harm. Reliable methods are essential to
find and remove sexist content, making online spaces safer and more welcoming.
Therefore, the sEXism Identification in Social neTworks (EXIST) challenge
addresses this issue at CLEF 2024. This study aims to improve sexism
identification in bilingual contexts (English and Spanish) by leveraging
natural language processing models. The tasks are to determine whether a text
is sexist and what the source intention behind it is. We fine-tuned the
XLM-RoBERTa model and separately used GPT-3.5 with few-shot learning prompts to
classify sexist content. The XLM-RoBERTa model exhibited robust performance in
handling complex linguistic structures, while GPT-3.5's few-shot learning
capability allowed for rapid adaptation to new data with minimal labeled
examples. Our approach using XLM-RoBERTa achieved 4th place in the soft-soft
evaluation of Task 1 (sexism identification). For Task 2 (source intention), we
achieved 2nd place in the soft-soft evaluation.
