---
title: "Who cites Measuring Political Bias in Large — 43 citing papers"
description: "43 papers of the Elamur Research library cite «Measuring Political Bias in Large Language Models: What Is Said and How It Is Said», found…"
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---
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[home](https://md.research.elamur.ai/free/en/) / [papers](https://md.research.elamur.ai/free/en/papers/) / [Measuring Political Bias in Large Language…](https://md.research.elamur.ai/free/en/papers/bang-2024-measuring-political-bias-large-language/) / cited by

# Who cites «Measuring Political Bias in Large Language Models: What Is Said and How It Is Said»

43 papers of our library cite it (of the 149,439 whose bibliographies we read) · 8 with a page here, the most cited first · [← back to «Measuring Political Bias in Large Language Models: What Is Said and How It Is Said»](https://md.research.elamur.ai/free/en/papers/bang-2024-measuring-political-bias-large-language/)

1. **[IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance](https://md.research.elamur.ai/free/en/papers/rottger-2026-issuebench-millions-realistic-prompts-measuring/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Paul Röttger, Musashi Hinck, Valentin Hofmann, et al.*2026 · 16 citations*
2. [**LLMs Struggle to Reject False Presuppositions when Misinformation Stakes are High arXiv:2505.22354**Judith Sieker, Clara Lachenmaier, Sina Zarrieß*2025 · 10 citations*](https://md.research.elamur.ai/free/en/papers/sieker-2025-llms-struggle-reject-false-presuppositions/)
3. **[Framing Political Bias in Multilingual LLMs Across Pakistani Languages](https://md.research.elamur.ai/free/en/papers/nadeem-2026-framing-political-bias-multilingual-llms/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Afrozah Nadeem, Mark Dras, Usman Naseem*2026 · 4 citations*
4. **[Democratic or Authoritarian? Probing a New Dimension of Political Biases in Large Language Models](https://md.research.elamur.ai/free/en/papers/piedrahita-2026-democratic-or-authoritarian-probing-new/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**David Guzman Piedrahita, Irene Strauss, Rada Mihalcea, et al.*2026 · 3 citations*
5. **[PsychEthicsBench: Evaluating Large Language Models Against Australian Mental Health Ethics](https://md.research.elamur.ai/free/en/papers/shen-2026-psychethicsbench-evaluating-large-language-models/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yaling Shen, Stephanie Fong, Yiwen Jiang, et al.*2026 · 2 citations*
6. **[When Do Language Models Endorse Limitations on Human Rights Principles?](https://md.research.elamur.ai/free/en/papers/samway-2026-when-do-language-models-endorse/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Keenan Samway, Miu Nicole Takagi, Rada Mihalcea, et al.*2026 · 1 citation*
7. **[Can Persona-Prompted LLMs Emulate Subgroup Values? An Empirical Analysis of Generalisability and Fairness in Cultural Alignment](https://md.research.elamur.ai/free/en/papers/tan-2026-can-persona-prompted-llms-emulate/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bryan Chen Zhengyu Tan, Zhengyuan Liu, Xiaoyuan Yi, et al.*2026*
8. **[News Credibility Assessment by LLMs and Humans: Implications for Political Bias](https://md.research.elamur.ai/free/en/papers/neves-2026-news-credibility-assessment-llms-humans/) [Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis](https://md.research.elamur.ai/free/en/venues/wassa/)**Pia Wenzel Neves, Charlott Jakob, Vera Schmitt*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/bang-2024-measuring-political-bias-large-language/) — Measuring Political Bias in Large Language Models: What Is Said and How It Is Said