Papers by Mohammed Fayiz Parappan

    1 papers
    Learning Subjective Label Distributions via Sociocultural Descriptors (2025.emnlp-main)

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    Challenge: Conventional approaches aggregate annotator judgements (labels), ignoring minority perspectives, and overlooking the influence of sociocultural context behind such annotations.
    Approach: They propose a framework where subjectivity in binary labels is modeled as an empirical distribution accounting for the variation in annotators through human values extracted from sociocultural descriptors using a language model.
    Outcome: The proposed model yields well-calibrated toxicity distribution predictions across binary toxicity labels, which are further used for majority label prediction across cultural subgroups.

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