Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities

Key points

• Ethnic and cultural identities rarely align with political borders, a tension made sharper by migration and long-standing cross-border ties.1
• We study nine such transborder ethnic communities along China's southwestern border with mainland Southeast Asia, looking at how large language models (LLMs) represent them.1
• We define and study identity ossification: a pattern where LLMs turn a fluid, cross-border ethnic identity into a single, fixed nation-state identity.1
• This decoupling suggests that ossification reflects narrative framing rather than simple knowledge absence, which matters for how we test cultural fairness in multilingual AI systems.1

Sources (1)

• [1]Knowing Is Not Telling: Identity Ossification in LLM Representations of Trans-border Communities
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 06:31 PM

  We introduce the Transborder Identity Ossification Diagnostic Framework (TIODF) that compares direct factual questions with open-ended narrative questions to test whether ossification comes from missing knowledge or from how models frame their answers.

  Ethnic and cultural identities rarely align with political borders, a tension made sharper by migration and long-standing cross-border ties.

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