Proposal: From One-Fit-All to Perspective Aware Modeling

Leixin Zhang

Keywords: perspectives, human label variation, personalized generation

Abstract: Variation in human annotation and human perspectives has drawn increasing attention in natural language processing research. Disagreement observed in data annotation challenges the conventional assumption of a single "ground truth" and uniform models trained on aggregated annotations, which tend to overlook minority viewpoints and individual perspectives. This proposal investigates three directions of perspective-oriented research: First, annotation formats that better capture the granularity and uncertainty of individual judgments; Second, annotation modeling that leverages socio-demographic features to better represent and predict underrepresented or minority perspectives; Third, personalized text generation that tailors outputs to individual users’ preferences and communicative styles. The proposed tasks aim to advance natural language processing research towards more faithfully reflecting the diversity of human interpretation, enhancing both inclusiveness and fairness in language technologies.

Archival Status: Archival

Paper Length: Long Paper (up to 8 pages of content)

Submission Number: 310