Homogenization in Language Models

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8 papers2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

The Multilingual Artificial Hivemind: Do All LLMs Think Alike in Every Language?
Oct 12, 2026Xaver Raphael Davey, Shubhra Mishra, Anton Adelöw +1Output Diversity in Language Models Homogenization in Language Models
KTH Royal Institute of Technology
How Strong Is the Evidence for the Artificial Hivemind? Reevaluating Evidence for the Open-Ended Homogeneity of Language Models
Sep 27, 2026Rylan Schaeffer, Brando Miranda, Joshua Kazdan +2Output Diversity in Language Models Homogenization in Language Models
Stanford Computer Science
Monocultural Biases: Correlated biases in large language models lead to unequal systemic exclusion rates in hiring
Aug 26, 2026Matthew Bone, Fabian Stephany, Maria del Rio-ChanonaHomogenization in Language Models Demographic Bias in Language Models
University of Oxford · Burning Glass Institute · University College London
Are LLMs becoming similarly creative? Evidence from three years of models
Aug 19, 2026Nirav Patel, Josiah Crossman, Eva Aggarwal +1Homogenization in Language Models Computational Creativity
Department of Computer Science, Duke University · Department of Electrical & Computer Engineering, Duke University
Thinking Like a Scientist? A Structural Study of LLM-Generated Research Methods
Jun 15, 2026Francesca Carlon, Brecht Verbeken, Vincent Ginis +1LLM Evaluation AI-Assisted Scientific Research
1Data Analytics Lab, Vrije Universiteit Brussel, Pleinlaan 5, 1050 Brussels, Belgium · 2imec-SMIT, Vrije Universiteit Brussel, Pleinlaan 9, 1050 Brussels, Belgium · School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, USA
Summarization is Not Dead Yet
Jun 6, 2026Dongqi Liu, Chenxi Whitehouse, Zheng Zhao +3LLM Evaluation Text Summarization
Saarland University, Max Planck Institute for Informatics · Tencent YouTu Research · University of Cambridge +2
The Alignment Tax: Response Homogenization in Aligned LLMs and Its Implications for Uncertainty Estimation
Mar 25, 2026Mingyi LiuHomogenization in Language Models LLM Alignment
Independent Researcher
The Homogenization Problem in LLMs: Towards Meaningful Diversity in AI Safety
Jan 3, 2026Ian Rios-SialerLLM Safety Homogenization in Language Models
Independent Researcher San Francisco, CA, USA

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The Multilingual Artificial Hivemind: Do All LLMs Think Alike in Every Language?

Xaver Raphael Davey, Shubhra Mishra, Anton Adelöw, Siyao Liu

KTH Royal Institute of Technology

Output Diversity in Language Models Homogenization in Language Models Multilingual Language Models Multilingual Language Model Evaluation

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Text generated by Large Language Models (LLMs) often remains limited in diversity compared to that produced by humans. Recent advances demonstrate the presence of inter- and intra-model collapse in open-ended generation of English text, raising concerns about the long-term homogenization of LLM, and more importantly, human thought. However, our understanding of whether this homogenization extends to languages beyond English is limited. In this work, we investigate the role of language on output diversity. Specifically, we aim to understand whether multilingual LLM-generated text is also homogeneous, and whether this homogeneity matches what we observe in humans. Across 23 open-weight models and 13 languages, we find that the artificial hivemind effect is pervasive but uneven in its extent. The intra-model effect holds in three different embedding spaces and under a lexical metric that uses no embedding model, so it is not an artifact of how diversity is measured. The size of the homogenization gap is associated with a model's competence in a language, measured on a held-out translation benchmark, robustly across embedding spaces. Thus, we demonstrate a performance-diversity tradeoff in multilingual LLMs.