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Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions

Paper · 2025 · Matthias Orlikowski, Jiaxin Pei, Paul Röttger et al.

Summary

Subjective annotation tasks often show annotator disagreement, and part of it is attributed to sociodemographic background. Zero-shot sociodemographic prompting of LLMs is known to work poorly. This paper asks whether fine-tuning can turn LLMs into accurate demographic models of annotators.

The authors built DEMO, which merges five existing subjective classification datasets (intimacy, offensiveness, politeness, safety, sentiment) with harmonised annotator attributes. They fine-tuned decoder-only LLMs, mainly Llama 3, using reward-model-style prediction heads. Annotator information enters through prompt-like input formats containing either demographics or an annotator ID.

Training does improve demographic prompting over baselines, but the gains mostly come from learning specific individuals. Improvements concentrate on annotators whose attribute combination is unique, so the attributes effectively act as an ID. On unseen annotators, neither demographics nor IDs outperform a text-only model. ID-based models also capture label disagreement better.

The work casts doubt on using LLMs to simulate how social groups perceive text without real behavioural examples.

Key points

  • DEMO dataset: 21,632 texts, 2,614 annotators and 147,648 annotations. Attributes are normalised to gender, age, race and education. Sources include the English subset of MINT (1,993 tweets, 261 annotators, 7 ratings each) and the POPQUORN offensiveness subset.
  • Research questions: Four RQs cover modelling known annotators, generalisation to new annotators via an annotator-based split, what demographic-tuned models actually learn, and predicting label distributions under disagreement.
  • Main finding: ID-based fine-tuning clearly beats both zero-shot and content-only baselines for known annotators. Demographics alone do not generalise.
  • Experimental setup: Llama 3 with 30 runs per configuration. A smaller check with Mistral 7B shows the same pattern.
  • Limitations and availability: All annotators are US-based, and only four attributes are covered. Data and code are available at github.com/morlikowski/beyond-demographics.

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

 Institutions

🎓Stanford UniversityStanford · United States434 works · 314,419 citations🎓University of MichiganAnn Arbor · United States70 works · 13,693 citations🎓Bocconi UniversityMilan · Italy28 works · 3,077 citations🎓Bielefeld UniversityBielefeld · Germany35 works · 1,196 citations

 Fields, topics and concepts

📚 Shelves 4The field of science this work belongs to (OpenAlex fields).Computer Science · 16,667 worksArtificial Intelligence · 16,194 worksArts and Humanities · 16,039 worksLanguage and Linguistics · 15,891 works

🧭 Topics 1Research subjects and languages the work is about.Language models & pretraining · 3,320 works

Cite

Cite as Matthias Orlikowski, Jiaxin Pei, Paul Röttger, Philipp Cimiano, David Jurgens and Dirk Hovy. 2025. Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions. Proceedings of the 63rd 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. DOI 10.18653/v1/2025.acl-long.104

@inproceedings{orlikowski2025beyond,
  title = {{Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions}},
  author = {Matthias Orlikowski, Jiaxin Pei, Paul Röttger, Philipp Cimiano, David Jurgens and Dirk Hovy},
  booktitle = {Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2025},
  doi = {10.18653/v1/2025.acl-long.104},
  url = {https://doi.org/10.18653/v1/2025.acl-long.104}
}

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