---
title: "Modeling Annotator Disagreement with Demographic-Aware"
description: "The authors propose DEM-MoE, a mixture-of-experts model whose routing depends on annotator… 81 references, cited by 1 paper here."
authors: ["Yinuo Xu", "Veronica Derricks", "Allison Earl", "David Jurgens"]
year: 2026
venue: "Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)"
source: "https://aclanthology.org/2026.acl-long.1914/"
doi: "10.18653/v1/2026.acl-long.1914"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/xu-2026-modeling-annotator-disagreement-demographic-aware/"
---
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# Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives

**Authors:** Yinuo Xu, Veronica Derricks, Allison Earl and [David Jurgens](https://md.research.elamur.ai/free/en/authors/david-jurgens/) · **Year:** 2026 · **Venue:** [Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)](https://md.research.elamur.ai/free/en/venues/acl/) ([ACL 2026](https://md.research.elamur.ai/free/en/venues/acl/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.acl-long.1914/) · [PDF](https://aclanthology.org/2026.acl-long.1914.pdf) · [DOI 10.18653/v1/2026.acl-long.1914](https://doi.org/10.18653/v1/2026.acl-long.1914) · [OpenAlex](https://openalex.org/W7166827056)

**Citations:** 0 citations (OpenAlex, 2026-10-14) · 1 in our library, of the 149,439 papers whose bibliographies we read

## Summary

In subjective labeling tasks such as toxicity, offensiveness or politeness rating, annotators often disagree. This divergence frequently follows social and demographic lines rather than being random noise. Pipelines that reduce labels to a single gold answer erase this variation. Existing annotator-aware models usually feed demographics into one shared network, with no structural push toward group-specific behavior.

The authors propose DEM-MoE, a mixture-of-experts model whose routing depends on annotator demographics. A ModernBERT encoding of the text is joined with Bayesian (Gaussian) embeddings of the annotator and their demographic attributes. This combined vector is dispatched to specialized experts, which predict that individual annotator's rating. Across five datasets, the model stays competitive for all demographic groups and performs best where disagreement is high.

To cope with sparse demographic coverage, the authors generate zero-shot, persona-prompted LLM annotations as imputed data. These agree moderately with human labels. The authors then compare ways of mixing real and synthetic data and find that the best strategy depends on dataset structure. The work supports NLP systems that keep minority viewpoints visible.

## Key points

- **Datasets:** The evaluation uses five datasets:
  - Toxicity (Kumar et al., 2021): 107,620 instances, 17,172 annotators.
  - Safety: 350 items, about 123 ratings each, α=0.241.
  - POPQUORN Politeness (highest agreement, α=0.440) and Offensiveness.
  - PCC: a new collection of doctor–patient interactions gathered via Prolific, with 2,230 instances, roughly 3.3 ratings per item and the highest mean entropy (1.492).
- **Architecture:** DEM-MoE builds on the MoE design of Shazeer et al. (2017). It samples annotator and demographic embeddings with the reparameterization trick and trains with a weighted loss that encourages balanced expert load and specialization.
- **Expert specialization:** Experts tend to specialize in particular subgroups. For example, one Politeness expert concentrates on women and on annotators without a high-school education. Intersectional identities are not modeled explicitly, which the authors list as a limitation.
- **Synthetic data:** Persona-based LLM labels are moderately aligned with human judgments. The authors advise using them sparingly, labeling them clearly, and never treating them as a replacement for real human annotators, especially for sensitive tasks like PCC.
- **Code:** The code is released at github.com/Nancy078/DEM-MoE.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

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## Cite

Cite as: Yinuo Xu, Veronica Derricks, Allison Earl and David Jurgens. 2026. Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{xu2026modeling,
  title = {{Modeling Annotator Disagreement with Demographic-Aware Experts and Synthetic Perspectives}},
  author = {Yinuo Xu, Veronica Derricks, Allison Earl and David Jurgens},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.1914},
  url = {https://doi.org/10.18653/v1/2026.acl-long.1914}
}
```

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*Updated 31 October 2026*