SubmissionNumber#=%=#15
FinalPaperTitle#=%=#HAMiSoN-Generative at ClimateActivism 2024: Stance Detection using generative large language models
ShortPaperTitle#=%=#
NumberOfPages#=%=#
CopyrightSigned#=%=#
JobTitle#==#
Organization#==#
Abstract#==#CASE in EACL 2024 proposes the shared task on Hate Speech and Stance Detection during Climate Activism. In our participation in the stance detection task, we have tested different approaches using LLMs for this classification task. We have tested a generative model using the classical seq2seq structure. Subsequently, we have considerably improved the results by replacing the last layer of these LLMs with a classifier layer. We have also studied how the performance is affected by the amount of data used in training. For this purpose, a partition of the dataset has been used and external data from posture detection tasks has been added.
Author{1}{Firstname}#=%=#Jesus M.
Author{1}{Lastname}#=%=#Fraile-Hernandez
Author{1}{Username}#=%=#jfraile
Author{1}{Email}#=%=#jfraile@lsi.uned.es
Author{1}{Affiliation}#=%=#NLP & IR Group, UNED
Author{2}{Firstname}#=%=#Anselmo
Author{2}{Lastname}#=%=#Peñas
Author{2}{Username}#=%=#anselmo
Author{2}{Email}#=%=#anselmo@lsi.uned.es
Author{2}{Affiliation}#=%=#NLP & IR Group, UNED

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