Four major LLMs show convergent bias in representing global writing systems

A new study published on arXiv reveals that major language models exhibit significant biases in their understanding and representation of the world's writing systems. Researchers developed the Digital Script Representation Index (DSRI) to measure digital support for scripts and found that only a small fraction of writing systems are fully supported by current digital infrastructure. Across four leading LLM families—Claude, GPT-4o, Grok, and DeepSeek—the models demonstrated highly convergent error patterns when assessing script features, particularly over-attributing religious use. This convergence suggests that historical imperial inequalities embedded in shared training corpora, rather than individual model design, are the primary drivers of these persistent biases. AI

IMPACT Reveals systemic biases in LLMs regarding global writing systems, highlighting the need for more equitable data and model development.

RANK_REASON The cluster contains a research paper detailing findings about LLM biases. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• arXiv
• Claude
• DeepSeek
• Digital Script Representation Index
• Global Script Database
• GPT-4o
• Grok
• Hiroki Fukui

• paper
• safety

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