---
title: "Simulating Social Attitudes with LLMs: Accuracy, Demographic"
description: "This study tests whether \"silicon sampling,\" where LLMs stand in for survey respondents, holds up for a sensitive policy area… 38 references."
authors: ["Cristina J. Perez", "Michael P. Vasquez Jr", "Philippe Giabbanelli", "Patrick Y. Wu"]
year: 2026
venue: "Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science"
source: "https://aclanthology.org/2026.nlpcss-1.12/"
doi: "10.18653/v1/2026.nlpcss-1.12"
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/perez-2026-simulating-social-attitudes-llms-accuracy/"
---
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# Simulating Social Attitudes with LLMs: Accuracy, Demographic Effects, and Refusal Behavior in the Sensitive Domain of Suicide Prevention

**Authors:** Cristina J. Perez, Michael P. Vasquez Jr, Philippe Giabbanelli and Patrick Y. Wu · **Year:** 2026 · **Venue:** [Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science](https://md.research.elamur.ai/free/en/venues/nlpcss/) ([Workshop on Natural Language Processing and Computational Social Science 2026](https://md.research.elamur.ai/free/en/venues/nlpcss/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.nlpcss-1.12/) · [PDF](https://aclanthology.org/2026.nlpcss-1.12.pdf) · [DOI 10.18653/v1/2026.nlpcss-1.12](https://doi.org/10.18653/v1/2026.nlpcss-1.12) · [OpenAlex](https://openalex.org/W7166889079)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
This study tests whether "silicon sampling," where LLMs stand in for survey respondents, holds up for a sensitive policy area: public support for suicide prevention measures such as firearm restrictions, crisis-line funding and mental-health spending. The authors compiled 32 items from seven nationally representative U.S. polls conducted in 2023–2025 and treated their published results as ground truth. They generated simulated answers while varying three factors:
- **Demographic persona:** race/ethnicity, gender, age, education, income and party.
- **Prompt framing:** direct elicitation, respondent embodiment or specialist embodiment.
- **Model:** four LLMs from different providers.

Simulated and real response distributions were compared at the level of single demographic subgroups. On average, the simulated distributions were far from the real ones. Adding demographic details shifted outputs considerably compared with persona-free prompts. Accuracy depended more on the model than on the framing, while refusal rates differed widely across both models and prompt designs. The authors conclude that it is unclear what population LLMs actually reflect on such questions. This casts doubt on using LLM-simulated respondents to estimate policy feasibility on sensitive topics.

## Key points
- **Scale:** 811,560 prompts in total. Mean absolute error between simulated and human response distributions was about 23 percentage points.
- **Models:** the abstract lists GPT-5 Nano, DeepSeek V3.2, Meta Llama 3.1 8B and Mistral Small 24B. The introduction names Qwen3-32B instead of Mistral, so the fourth model should be checked against the full text.
- **Ground truth:** sources include AFSP's poll on attitudes toward mental health and suicide prevention, Duke's 988 Awareness Survey, a YouGov Gun Ownership Survey and a help-seeking preferences survey.
- **Refusals:** refusal behavior (RQ3) varied sharply by model and framing, which the authors link to guardrails around self-harm topics.
- **Limitation:** the surveys report only single-attribute marginals. The LLM aggregation weights all persona combinations equally, whereas the survey marginals reflect the sample's actual composition, and this cannot be corrected without joint distributions.

*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

- [American University](https://md.research.elamur.ai/free/en/universities/american-university/) — University, Washington, United States · 2 works · 6,540 citations

Also on the byline: [Old Dominion University](https://ror.org/04zjtrb98).

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## Cite

Cite as: Cristina J. Perez, Michael P. Vasquez Jr, Philippe Giabbanelli and Patrick Y. Wu. 2026. Simulating Social Attitudes with LLMs: Accuracy, Demographic Effects, and Refusal Behavior in the Sensitive Domain of Suicide Prevention. Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science.

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

```bibtex
@inproceedings{perez2026simulating,
  title = {{Simulating Social Attitudes with LLMs: Accuracy, Demographic Effects, and Refusal Behavior in the Sensitive Domain of Suicide Prevention}},
  author = {Cristina J. Perez, Michael P. Vasquez Jr, Philippe Giabbanelli and Patrick Y. Wu},
  booktitle = {Proceedings of the Seventh Workshop on Natural Language Processing and Computational Social Science},
  year = {2026},
  doi = {10.18653/v1/2026.nlpcss-1.12},
  url = {https://doi.org/10.18653/v1/2026.nlpcss-1.12}
}
```

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## References

38 references, 4 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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36. Bazen Gashaw Teferra, Nabil Johny, Sandra Huang, Alice Rueda, Mohammad Amin Kamaleddin, Katharine Dunlop, Yanbo Zhang, Manish Jha, Divya Sharma, and Venkat Bhat. 2026. Assessing the impact of safety guardrails on large language models using irritability metrics. npj Digital Medicine. [find](https://scholar.google.com/scholar?q=Bazen+Gashaw+Teferra%2C+Nabil+Johny%2C+Sandra+Huang%2C+Alice+Rueda%2C+Mohammad+Amin+Kamaleddin%2C+Katharine+Dunlop%2C+Yanbo+Zhang%2C+Manish+Jha%2C+Divya+Sharma%2C+and+Venkat+Bhat%2E+2026%2E+Assessing+the+impact+of+safety+g)
37. Lindia Tjuatja, Valerie Chen, Tongshuang Wu, Ameet Talwalkwar, and Graham Neubig. 2024. [Do llms exhibit human-like response biases? a case study in survey design](https://md.research.elamur.ai/free/en/papers/tjuatja-2024-do-llms-exhibit-human-like/). Transactions of the Association for Computational Linguistics, 12:1011–1026.
38. Stephen Zhong, Nathalie Japkowicz, and Philippe Giabbanelli. 2025b. Do we still need people? comparing human and llm personas in political modeling and simulation. In 2025 ACM/IEEE 28th International Conference on Model Driven Engineering Languages and Systems Companion (MODELS-C), pages 512– 521. I… [find](https://scholar.google.com/scholar?q=Stephen+Zhong%2C+Nathalie+Japkowicz%2C+and+Philippe+Giabbanelli%2E+2025b%2E+Do+we+still+need+people%3F+comparing+human+and+llm+personas+in+political+modeling+and+simulation%2E+In+2025+ACM%2FIEEE+28th+International+)

*Updated 2 November 2026*