---
title: "Who cites On The Origin of Cultural Biases — 13 citing papers"
description: "13 papers of the Elamur Research library cite «On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic…"
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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/) / [On The Origin of Cultural Biases in Language…](https://md.research.elamur.ai/free/en/papers/naous-2025-origin-cultural-biases-language-models/) / cited by

# Who cites «On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena»

13 papers of our library cite it (of the 149,439 whose bibliographies we read) · 6 with a page here, the most cited first · [← back to «On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena»](https://md.research.elamur.ai/free/en/papers/naous-2025-origin-cultural-biases-language-models/)

1. **[Igniting Creative Writing in Small Language Models: LLM-as-a-Judge versus Multi-Agent Refined Rewards](https://md.research.elamur.ai/free/en/papers/wei-2025-igniting-creative-writing-small-language/) [Conference on Empirical Methods in Natural Language Processing (EMNLP)](https://md.research.elamur.ai/free/en/venues/emnlp/)**Xiaolong Wei, Bo Lu, Xingyu Zhang, et al.*2025 · 10 citations*
2. **[From RAG to Agentic RAG for Faithful Islamic Question Answering](https://md.research.elamur.ai/free/en/papers/bhatia-2026-rag-agentic-rag-faithful-islamic/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Gagan Bhatia, Hamdy Mubarak, Mustafa Jarrar, et al.*2026 · 8 citations*
3. **[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*
4. **[Defining Cultural Capabilities for AI Evaluation: A Taxonomy Grounded in Intercultural Communication Theory](https://md.research.elamur.ai/free/en/papers/nejadgholi-2026-defining-cultural-capabilities-ai-evaluation/) [Workshop on Evaluating Evaluations (EvalEval)](https://md.research.elamur.ai/free/en/venues/evaleval/)**Isar Nejadgholi, Masoud Kianpour, Krishnapriya Vishnubhotla, et al.*2026*
5. **[Mitigating Cultural Bias in LLMs via Multi-Agent Cultural Debate](https://md.research.elamur.ai/free/en/papers/tan-2026-mitigating-cultural-bias-llms-multi/) [Findings of the Association for Computational Linguistics](https://md.research.elamur.ai/free/en/venues/findings/)**Qian Tan, Lei Jiang, Yuting Zeng, et al.*2026*
6. [**The Algorithmic Unconscious: Structural Mechanisms and Implicit Biases in Large Language Models arXiv:2602.18468**Philippe Boisnard*2026*](https://md.research.elamur.ai/free/en/papers/boisnard-2026-algorithmic-unconscious-structural-mechanisms-implicit/)

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

- [← back to](https://md.research.elamur.ai/free/en/papers/naous-2025-origin-cultural-biases-language-models/) — On The Origin of Cultural Biases in Language Models: From Pre-training Data to Linguistic Phenomena