---
title: "Who cites The Ghost in the Machine has an — 65 citing papers"
description: "65 papers of the Elamur Research library cite «The Ghost in the Machine has an American accent: value conflict in GPT-3», found in their…"
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---
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# Who cites «The Ghost in the Machine has an American accent: value conflict in GPT-3»

65 papers of our library cite it (of the 149,439 whose bibliographies we read) · 32 with a page here, the most cited first · [← back to «The Ghost in the Machine has an American accent: value conflict in GPT-3»](https://md.research.elamur.ai/free/en/papers/johnson-2022-ghost-machine-has-american-accent/)

1. [**Cultural Bias and Cultural Alignment of Large Language Models arXiv:2311.14096 · PNAS Nexus, Volume 3, Issue 9, September 2024, pgae346**Yan Tao, Olga Viberg, Ryan S. Baker, et al.*2023 · 470 citations*](https://md.research.elamur.ai/free/en/papers/tao-2023-cultural-bias-cultural-alignment-large/)
2. **[Assessing Cross-Cultural Alignment between ChatGPT and Human Societies: An Empirical Study](https://md.research.elamur.ai/free/en/papers/cao-2023-assessing-cross-cultural-alignment-between/) [Workshop on Cross-Cultural Considerations in NLP](https://md.research.elamur.ai/free/en/venues/c3nlp/)**Yong Cao, Li Zhou, Seolhwa Lee, et al.*2023 · 279 citations*
3. **[Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://md.research.elamur.ai/free/en/papers/naous-2023-having-beer-after-prayer-measuring/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tarek Naous, Michael J. Ryan, Alan Ritter, et al.*2023 · 272 citations*
4. [**Towards Measuring the Representation of Subjective Global Opinions in Language Models arXiv:2306.16388**Esin Durmus, Karina Nguyen, Thomas I. Liao, et al.*2023 · 270 citations*](https://md.research.elamur.ai/free/en/papers/durmus-2023-towards-measuring-representation-subjective-global/)
5. [**Trustworthy LLMs: a Survey and Guideline for Evaluating Large Language Models' Alignment arXiv:2308.05374**Yang Liu; Yuanshun Yao; Jean-Francois Ton; Xiaoying Zhang; Ruocheng Guo; Hao Cheng; Yegor Klochkov; Muhammad Faaiz Taufiq;, Hang Li*2023 · 223 citations*](https://md.research.elamur.ai/free/en/papers/liu-2023-trustworthy-llms-survey-guideline-evaluating/)
6. **[CultureLLM: Incorporating Cultural Differences into Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-culturellm-incorporating-cultural-differences-large/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Cheng Li, Mengzhou Chen, Jindong Wang, et al.*2024 · 162 citations*
7. **[Probing Pre-Trained Language Models for Cross-Cultural Differences in Values](https://md.research.elamur.ai/free/en/papers/arora-2023-probing-pre-trained-language-models/) [Workshop on Cross-Cultural Considerations in NLP](https://md.research.elamur.ai/free/en/venues/c3nlp/)**Arnav Arora, Lucie-aimée Kaffee, Isabelle Augenstein*2023 · 156 citations*
8. **[Language Model Behavior: A Comprehensive Survey](https://md.research.elamur.ai/free/en/papers/chang-2024-language-model-behavior-comprehensive-survey/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Tyler A. Chang, Benjamin K. Bergen*2024 · 148 citations*
9. **[Survey of Cultural Awareness in Language Models: Text and Beyond](https://md.research.elamur.ai/free/en/papers/pawar-2025-survey-cultural-awareness-language-models/) [Computational Linguistics](https://md.research.elamur.ai/free/en/venues/cl/)**Siddhesh Pawar, Junyeong Park, Jiho Jin, et al.*2025 · 106 citations*
10. **[Towards Measuring and Modeling “Culture” in LLMs: A Survey](https://md.research.elamur.ai/free/en/papers/adilazuarda-2024-measuring-modeling-culture-llms-survey/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Muhammad Farid Adilazuarda, Sagnik Mukherjee, Pradhyumna Lavania, et al.*2024 · 103 citations*
11. **[Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://md.research.elamur.ai/free/en/papers/naous-2024-having-beer-after-prayer-measuring/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Tarek Naous, Michael J Ryan, Alan Ritter, et al.*2024 · 96 citations*
12. **[Not All Countries Celebrate Thanksgiving: On the Cultural Dominance in Large Language Models](https://md.research.elamur.ai/free/en/papers/wang-2024-not-all-countries-celebrate-thanksgiving/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Wenxuan Wang, Wenxiang Jiao, Jingyuan Huang, et al.*2024 · 96 citations*
13. **[Moral Foundations of Large Language Models](https://md.research.elamur.ai/free/en/papers/abdulhai-2024-moral-foundations-large-language-models/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Marwa Abdulhai, Gregory Serapio-García, Clement Crepy, et al.*2024 · 94 citations*
14. **[Culturally Aware and Adapted NLP: A Taxonomy and a Survey of the State of the Art](https://md.research.elamur.ai/free/en/papers/liu-2025-culturally-aware-adapted-nlp-taxonomy/) [Transactions of the Association for Computational Linguistics (TACL)](https://md.research.elamur.ai/free/en/venues/tacl/)**Chen Cecilia Liu, Iryna Gurevych, Anna Korhonen*2025 · 90 citations*
15. [**Foundational Challenges in Assuring Alignment and Safety of Large Language Models arXiv:2404.09932**Usman Anwar, Abulhair Saparov, Javier Rando, et al.*2024 · 87 citations*](https://md.research.elamur.ai/free/en/papers/anwar-2024-foundational-challenges-assuring-alignment-safety/)
16. **[CulturePark: Boosting Cross-cultural Understanding in Large Language Models](https://md.research.elamur.ai/free/en/papers/li-2024-culturepark-boosting-cross-cultural-understanding/) [Conference on Neural Information Processing Systems (NeurIPS)](https://md.research.elamur.ai/free/en/venues/neural-information-processing-systems/)**Cheng Li, Damien Teney, Linyi Yang, et al.*2024 · 70 citations*
17. **[Culturally Aware Natural Language Inference](https://md.research.elamur.ai/free/en/papers/huang-2023-culturally-aware-natural-language-inference/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Jing Huang, Diyi Yang*2023 · 61 citations*
18. **[NormAd: A Framework for Measuring the Cultural Adaptability of Large Language Models](https://md.research.elamur.ai/free/en/papers/rao-2025-normad-framework-measuring-cultural-adaptability/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Abhinav Rao, Akhila Yerukola, Vishwa Shah, et al.*2025 · 46 citations*
19. **[PalmX 2025: The First Shared Task on Benchmarking LLMs on Arabic and Islamic Culture](https://md.research.elamur.ai/free/en/papers/alwajih-2025-palmx-2025-first-shared-task/) [Arabic Natural Language Processing Conference (ArabicNLP)](https://md.research.elamur.ai/free/en/venues/arabicnlp/)**Fakhraddin Alwajih, Abdellah El Mekki, Hamdy Mubarak, et al.*2025 · 23 citations*
20. **[JAWAHER: A Multidialectal Dataset of Arabic Proverbs for LLM Benchmarking](https://md.research.elamur.ai/free/en/papers/magdy-2025-jawaher-multidialectal-dataset-arabic-proverbs/) [Conference of the North American Chapter of the Association for Computational Linguistics (NAACL)](https://md.research.elamur.ai/free/en/venues/naacl/)**Samar Mohamed Magdy, Sang Yun Kwon, Fakhraddin Alwajih, et al.*2025 · 18 citations*
21. **[Bridging Cultural Nuances in Dialogue Agents through Cultural Value Surveys](https://md.research.elamur.ai/free/en/papers/cao-2024-bridging-cultural-nuances-dialogue-agents/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Yong Cao, Min Chen, Daniel Hershcovich*2024 · 16 citations*
22. **[AboutMe: Using Self-Descriptions in Webpages to Document the Effects of English Pretraining Data Filters](https://md.research.elamur.ai/free/en/papers/lucy-2024-aboutme-self-descriptions-webpages-document/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Li Lucy, Suchin Gururangan, Luca Soldaini, et al.*2024 · 15 citations*
23. **[Tailored Emotional LLM-Supporter: Enhancing Cultural Sensitivity](https://md.research.elamur.ai/free/en/papers/liu-2026-tailored-emotional-llm-supporter-enhancing/) [Conference of the European Chapter of the Association for Computational Linguistics (EACL)](https://md.research.elamur.ai/free/en/venues/eacl/)**Chen Cecilia Liu, Hiba Arnaout, Nils Kovačić, et al.*2026 · 1 citation*
24. [**Tracing English interference in AI-generated German AI-Linguistica 5**Paolo Valentinelli*2026 · 1 citation*](https://md.research.elamur.ai/free/en/papers/valentinelli-2026-tracing-english-interference-ai-generated/)
25. [**AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation arXiv:2506.14634 · Survey Research Methods (2025)**Leah von der Heyde, Anna-Carolina Haensch, Bernd Weiß, et al.*2025 · 1 citation*](https://md.research.elamur.ai/free/en/papers/heyde-2025-ain-t-nothing-but-survey/)
26. **[AYN: A Tiny Yet Competitive Indian Legal Language Model Pretrained from Scratch](https://md.research.elamur.ai/free/en/papers/niyogi-2026-ayn-tiny-yet-competitive-indian/) [Language Resources and Evaluation Conference (LREC)](https://md.research.elamur.ai/free/en/venues/lrec/)**Mitodru Niyogi, Eric Gaussier, Arnab Bhattacharya*2026*
27. [**Beyond Cultural Knowledge: Evaluating Arabic Cultural Appropriateness of Large Language Models arXiv:2609.16006**Enes Altinisik, Hamdy Mubarak, Masoomali Fatehkia, et al.*2026*](https://md.research.elamur.ai/free/en/papers/altinisik-2026-cultural-knowledge-evaluating-arabic-cultural/)
28. **[Beyond Monolithic Culture: Evaluating Understandability of Online Text Across Cultural Dimensions](https://md.research.elamur.ai/free/en/papers/pandey-2026-beyond-monolithic-culture-evaluating-understandability/) [Workshop on Cross-Cultural Considerations in NLP](https://md.research.elamur.ai/free/en/venues/c3nlp/)**Saurabh Kumar Pandey, Harshit Gupta, Sougata Saha, et al.*2026*
29. **[DVMap: Fine-Grained Pluralistic Value Alignment via High-Consensus Demographic-Value Mapping](https://md.research.elamur.ai/free/en/papers/zhu-2026-dvmap-fine-grained-pluralistic-value/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Pengyun Zhu, Yuqi Ren, Zhen Wang, et al.*2026*
30. **[Empathy Applicability Modeling for General Health Queries](https://md.research.elamur.ai/free/en/papers/randhawa-2026-empathy-applicability-modeling-general-health/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Shan Randhawa, Agha Ali Raza, Kentaro Toyama, et al.*2026*
31. **[InsideOut: Measuring and Mitigating Insider–Outsider Bias in Interview Script Generation](https://md.research.elamur.ai/free/en/papers/wan-2026-insideout-measuring-mitigating-insider-outsider/) [Annual Meeting of the Association for Computational Linguistics (ACL)](https://md.research.elamur.ai/free/en/venues/acl/)**Yixin Wan, Xingrun Chen, Kai-Wei Chang*2026*
32. **[Reflexive Research with LLMs: Considering the Positionality of Users and Systems](https://md.research.elamur.ai/free/en/papers/smith-2026-reflexive-research-llms-considering-positionality/) [Shaping Multilingual, Multimodal AI for the Social Sciences and Humanities @ LREC](https://md.research.elamur.ai/free/en/venues/llms4ssh/)**Eleanor L.T. Smith, Luis Morgado da Costa, Antske Fokkens*2026*

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

- [← back to](https://md.research.elamur.ai/free/en/papers/johnson-2022-ghost-machine-has-american-accent/) — The Ghost in the Machine has an American accent: value conflict in GPT-3
