NepPlural: A Human-Anchored Benchmark for Persona Annotation in Nepali Migration Discourse

Jinu Nyachhyon, Anish Khatiwada, Bimal Timilsina

Pluralistic alignment asks that language models faithfully represent a society's diverse, competing values rather than collapse them into one dominant viewpoint, yet flagship pluralistic-alignment datasets remain heavily skewed toward Western respondents. We introduce NepPlural, a human-anchored benchmark for modelling competing socio-cultural personas in Nepali public discourse on youth migration and brain drain. We curate 2,148 code-mixed (Devanagari, Romanized Nepali, English) YouTube comments, annotate them along a four-axis persona taxonomy (Intent, Primary Driver, Value Orientation, Affect) plus an explicit No-Persona class, and build a 300-comment gold set labelled independently by three native Nepali speakers (mean Fleiss' $κ\approx 0.55$). Using this gold set to audit automatic annotation, we find that LLM annotation reliability is driven by protocol. Labels collected in batches through chat interfaces reach only near-chance inter-model agreement ($κ\approx 0.17$). Annotating one comment at a time raises agreement above the native-speaker level ($κ\approx 0.72$), but consistency is not validity: under a forced-choice schema, models agree on personas for the 39% of…