---
title: "Who cites Specializing Large Language — 14 citing papers"
description: "14 papers of the Elamur Research library cite «Specializing Large Language Models to Simulate Survey Response Distributions for Global…"
canonical: "https://research.elamur.ai/free/en/papers/cao-2025-specializing-large-language-models-simulate/cited-by/"
---
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# Who cites «Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations»

14 papers of our library cite it (of the 149,439 whose bibliographies we read) · 7 with a page here, the most cited first · [← back to «Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations»](https://md.research.elamur.ai/free/en/papers/cao-2025-specializing-large-language-models-simulate/)

1. **[Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinions](https://md.research.elamur.ai/free/en/papers/suh-2025-language-model-fine-tuning-scaled/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Joseph Suh, Erfan Jahanparast, Suhong Moon, et al.*2025 · 28 citations*
2. **[Beyond Demographics: Fine-tuning Large Language Models to Predict Individuals' Subjective Text Perceptions](https://md.research.elamur.ai/free/en/papers/orlikowski-2025-beyond-demographics-fine-tuning-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Matthias Orlikowski, Jiaxin Pei, Paul Röttger, et al.*2025 · 22 citations*
3. **[Algorithmic Fidelity of Large Language Models in Generating Synthetic German Public Opinions: A Case Study](https://md.research.elamur.ai/free/en/papers/ma-2025-algorithmic-fidelity-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bolei Ma, Berk Yoztyurk, Anna-Carolina Haensch, et al.*2025 · 10 citations*
4. **[German General Social Survey Personas: A Survey-Derived Persona Prompt Collection for Population-Aligned LLM Studies](https://md.research.elamur.ai/free/en/papers/rupprecht-2026-german-general-social-survey-personas/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Jens Rupprecht, Leon Froehling, Claudia Wagner, et al.*2026 · 1 citation*
5. [**Learning from Convenience Samples: A Case Study on Fine-Tuning LLMs for Survey Non-response in the German Longitudinal Election Study arXiv:2509.25063**Tobias Holtdirk, Dennis Assenmacher, Arnim Bleier, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/holtdirk-2025-learning-convenience-samples-case-study/)
6. **[Distributional Alignment for Large Language Models under Domain Shift](https://md.research.elamur.ai/free/en/papers/pham-2026-distributional-alignment-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Viet Thanh Pham, Lizhen Qu, Zhuang Li, et al.*2026*
7. **[Too Open for Opinion? Embracing Open-Endedness in Large Language Models for Social Simulation](https://md.research.elamur.ai/free/en/papers/ma-2026-too-open-opinion-embracing-open/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Bolei Ma, Yong Cao, Indira Sen, et al.*2026*

7 more papers of our library cite it; they have no page here yet.

- [← back to](https://md.research.elamur.ai/free/en/papers/cao-2025-specializing-large-language-models-simulate/) — Specializing Large Language Models to Simulate Survey Response Distributions for Global Populations