Learning Who Disagrees: Demographic Importance Weighting for Modeling Annotator Distributions with DiADEM
When humans label subjective content, they disagree, and that disagreement is not noise. It reflects genuine differences in perspective shaped by annotators' social identities and lived experiences. Yet standard practice still flattens these judgments into a single majority label, and recent LLM-based approaches fare no better: we show that prompted large language models, even with chain-of-thought reasoning, fail to recover the structure of human disagreement. We introduce DiADEM, a neural architecture that learns "how much each demographic axis matters" for predicting who will disagree and on what. DiADEM encodes annotators through per-demographic projections governed by a learned impor...
| Authors | Samay U. Shetty, Tharindu Cyril Weerasooriya, Deepak Pandita et al. |
|---|---|
| Primary category | cs.AI |
| Published | 9 April 2026 |
| Last updated at source | 9 April 2026 |
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