Beyond Single Models: Leveraging LLM Ensembles for Human Value Detection in Text - Rodrigues, Diego Dimer; Recamonde-Mendoza, Mariana; Moreira, Viviane P. (Anais do Simpósio Brasileiro de Tecnologia da Informação e da Linguagem Humana (STIL); 2024: Anais do XV Simpósio Brasileiro de Tecnologia da Informação e da Linguagem HumanaDO - 10.5753/stil.2024.245441, 2024)
Every text may reflect its writer’s opinions, and these opinions, especially in political contexts, are often tied to specific human values that they either attain or constrain. Identifying these values can provide policymakers with deeper insights into the underlying factors that influence public discourse and decision-making. While current large language models (LLMs) have shown promise across various tasks, no single model may generalize sufficiently to excel in tasks like human value detection. In this work, we utilize data from the Human Value Detection task at CLEF 2024 and propose leveraging multiple ensembles of LLMs to enhance the identification of human values in text. Our results found that the ensemble models achieved higher F1 scores than all baseline models, suggesting that combining multiple models can offer performance comparable to very large models but at much lower memory requirements.