@inproceedings{lee-goldwasser-2025-solar,
    title = "{SOLAR}: Towards Characterizing Subjectivity of Individuals through Modeling Value Conflicts and Trade-offs",
    author = "Lee, Younghun  and
      Goldwasser, Dan",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-luhme/2025.emnlp-main.1053/",
    doi = "10.18653/v1/2025.emnlp-main.1053",
    pages = "20847--20862",
    ISBN = "979-8-89176-332-6",
    abstract = "Large Language Models (LLMs) not only have solved complex reasoning problems but also exhibit remarkable performance in tasks that require subjective decision-making. Existing studies suggest that LLM generations can convey subjectivity to some extent, yet exploring whether LLMs can account for individual-level subjectivity has not been sufficiently studied. In this paper, we characterize the subjectivity of individuals on social media and infer their moral judgments using LLMs. We propose a framework, SolAr (Subjective Ground with Value Abstraction), that observes value conflicts and trade-offs in the user-generated texts to better represent subjective ground of individuals. Empirical results demonstrate that our framework enhances overall inference performance, with notable improvements for users with limited data and in controversial situations. Additionally, we qualitatively show that SolAr provides explanations about individuals' value preferences, which can further account for their judgments."
}