---
title: "Who cites Musashi Hinck — 112 papers of our library"
description: "112 papers of the Elamur Research library cite the works of Musashi Hinck, found in their bibliographies; the most cited first."
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---
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# Who cites Musashi Hinck

124 citations of their works from 112 papers of our library (of the 149,439 whose bibliographies we read) · 28 with a page here, the most cited first · [← Musashi Hinck](https://md.research.elamur.ai/free/en/authors/musashi-hinck/)

1. [**XSTest: A Test Suite for Identifying Exaggerated Safety Behaviours in Large Language Models arXiv:2308.01263**Paul Rottger, Hannah Rose Kirk, Bertie Vidgen, et al.*2023 · 244 citations*](https://md.research.elamur.ai/free/en/papers/rottger-2023-xstest-test-suite-identifying-exaggerated/)
2. **[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/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yejin Bang, Delong Chen, Nayeon Lee, et al.*2024 · 76 citations*
3. **[Pragmatics in the Era of Large Language Models: A Survey on Datasets, Evaluation, Opportunities and Challenges](https://md.research.elamur.ai/free/en/papers/ma-2025-pragmatics-era-large-language-models/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Bolei Ma, Yuting Li, Wei Zhou, et al.*2025 · 49 citations*
4. [**Auxiliary task demands mask the capabilities of smaller language models arXiv:2404.02418**Jennifer Hu, Michael C. Frank*2024 · 41 citations*](https://md.research.elamur.ai/free/en/papers/hu-2024-auxiliary-task-demands-mask-capabilities/)
5. **[Benchmarking Distributional Alignment of Large Language Models](https://md.research.elamur.ai/free/en/papers/meister-2025-benchmarking-distributional-alignment-large-language/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Nicole Meister, Carlos Guestrin, Tatsunori Hashimoto*2025 · 35 citations*
6. **[Biased LLMs can Influence Political Decision-Making](https://md.research.elamur.ai/free/en/papers/fisher-2025-biased-llms-can-influence-political/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Jillian Fisher, Shangbin Feng, Robert Aron, et al.*2025 · 20 citations*
7. **[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*
8. **[Mind the Value-Action Gap: Do LLMs Act in Alignment with Their Values?](https://md.research.elamur.ai/free/en/papers/shen-2025-mind-value-action-gap-do/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Hua Shen, Nicholas Clark, Tanu Mitra*2025 · 16 citations*
9. **[Explainability and Interpretability of Multilingual Large Language Models: A Survey](https://md.research.elamur.ai/free/en/papers/resck-2025-explainability-interpretability-multilingual-large-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Lucas Resck, Isabelle Augenstein, Anna Korhonen*2025 · 11 citations*
10. **[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*
11. **[Aligned but Blind: Alignment Increases Implicit Bias by Reducing Awareness of Race](https://md.research.elamur.ai/free/en/papers/sun-2025-aligned-but-blind-alignment-increases/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Lihao Sun, Chengzhi Mao, Valentin Hofmann, et al.*2025 · 10 citations*
12. **[Locate, Steer, and Improve: A Practical Survey of Actionable Mechanistic Interpretability in Large Language Models](https://md.research.elamur.ai/free/en/papers/zhang-2026-locate-steer-improve-practical-survey/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Hengyuan Zhang, Zhihao Zhang, Ercong Nie, et al.*2026 · 9 citations*
13. **[Centurio: On Drivers of Multilingual Ability of Large Vision-Language Model](https://md.research.elamur.ai/free/en/papers/geigle-2025-centurio-drivers-multilingual-ability-large/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Gregor Geigle, Florian Schneider, Carolin Holtermann, et al.*2025 · 9 citations*
14. **[Do Political Opinions Transfer Between Western Languages? An Analysis of Unaligned and Aligned Multilingual LLMs](https://md.research.elamur.ai/free/en/papers/weeber-2026-do-political-opinions-transfer-between/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Franziska Weeber, Tanise Ceron, Sebastian Padó*2026 · 4 citations*
15. **[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*
16. **[The Hidden Bias: A Study on Explicit and Implicit Political Stereotypes in Large Language Models](https://md.research.elamur.ai/free/en/papers/lohr-2026-hidden-bias-study-explicit-implicit/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Konrad Löhr, Shuzhou Yuan, Michael Färber*2026 · 2 citations*
17. [**MÖVE: A Holistic LLM Benchmark for the German Public Sector arXiv:2606.13111**Camilla Dalerci, Thilo Michael, Robin Schaefer, et al.*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/dalerci-2026-m-ove-holistic-llm-benchmark/)
18. **[Prompt Perturbations Reveal Human-Like Biases in Large Language Model Survey Responses](https://md.research.elamur.ai/free/en/papers/rupprecht-2026-prompt-perturbations-reveal-human-like/) [Workshop on Natural Language Processing and Computational Social Science](https://md.research.elamur.ai/free/en/venues/nlpcss/)**Jens Rupprecht, Georg Ahnert, Markus Strohmaier*2026 · 1 citation*
19. **[The Pluralistic Moral Gap: Understanding Moral Judgment and Value Differences between Humans and Large Language Models](https://md.research.elamur.ai/free/en/papers/russo-2026-pluralistic-moral-gap-understanding-moral/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Giuseppe Russo, Debora Nozza, Paul Röttger, et al.*2026 · 1 citation*
20. **[Towards Simulating Social Media Users with LLMs: Evaluating the Operational Validity of Conditioned Comment Prediction](https://md.research.elamur.ai/free/en/papers/schwager-2026-simulating-social-media-users-llms/) [Workshop on Computational Approaches to Subjectivity, Sentiment and Social Media Analysis](https://md.research.elamur.ai/free/en/venues/wassa/)**Nils Schwager, Simon Münker, Alistair Plum, et al.*2026 · 1 citation*
21. **[\textitOne Persona, \textitMany Cues, \textitDifferent Results: How Sociodemographic Cues Impact LLM Personalization](https://md.research.elamur.ai/free/en/papers/weeber-2026-textitone-persona-textitmany-cues-textitdifferent/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Franziska Weeber, Vera Neplenbroek, Jan Batzner, et al.*2026*
22. **[Carefully Considering Culture: Analyzing LLM Alignment in Single- and Multi-Cultural Settings using Cultural Consensus Theory](https://md.research.elamur.ai/free/en/papers/pothugunta-2026-carefully-considering-culture-analyzing-llm/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Krishna Pothugunta, John P. Lalor*2026*
23. **[Do Language Models Think Consistently? A Study of Value Preferences Across Varying Response Lengths](https://md.research.elamur.ai/free/en/papers/nair-2026-do-language-models-think-consistently/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Inderjeet Jayakumar Nair, Lu Wang*2026*
24. [**From Global Benchmarks to Local Evaluations: Benchmarking LLMs for the German Public Sector arXiv:2608.17827**Camilla Dalerci, Thilo Michael, Robin Schaefer, et al.*2026*](https://md.research.elamur.ai/free/en/papers/dalerci-2026-global-benchmarks-local-evaluations-benchmarking/)
25. **[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*
26. **[R.U.Psycho? A Framework for Robust Unified Psychometric Testing of Language Models](https://md.research.elamur.ai/free/en/papers/schelb-2026-r-u-psycho-framework-robust/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Julian Schelb, Orr Borin, David Garcia, et al.*2026*
27. **[Teaching Values to Machines: Simulating Human-Like Behavior in LLMs](https://md.research.elamur.ai/free/en/papers/yehudai-2026-teaching-values-machines-simulating-human/) [Workshop on Generation, Evaluation and Metrics (GEM)](https://md.research.elamur.ai/free/en/venues/gem/)**Asaf Yehudai, Naama Rozen, Ariel Gera*2026*
28. **[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*

84 more citing papers of our library have no page here yet.

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