Benchmarking Open-Weight Foundation Models for Global AI Technical Governance
Large language models (LLMs) are increasingly deployed in artificial intelligence (AI) governance analysis across national and international organisations. There is, however, growing evidence that such models produce significantly less accurate responses for countries that are underrepresented in their training data-a pattern described in existing literature as geographic bias. Existing studies examining this phenomenon are subject to three methodological limitations that together undermine their findings: (1) reliance on proprietary systems whose weights are not publicly released, which prevents independent replication; (2) evaluation of model knowledge about years that fall after data c...
| Authors | Jason Hung |
|---|---|
| Primary category | cs.CY |
| Published | 12 April 2026 |
| Last updated at source | 12 April 2026 |
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