LLMs Substitute State Policy Values, Study Finds

A new study published on arXiv investigates how Large Language Models (LLMs) like Claude Sonnet 5.5 and GPT-5.6 Sol handle state-specific policy questions. The research found that these models frequently substituted a value from one U.S. state for another when asked about specific state policies, particularly concerning Medicaid income eligibility. While the models showed a tendency to reproduce another state's value reproducibly, the study highlights the fragility of attribution and the need for comprehensive same-state reference sets to accurately assess cross-jurisdiction errors. AI

IMPACT Highlights potential inaccuracies in LLM recall of specific policy data, impacting applications requiring precise jurisdictional information.

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

Read on arXiv cs.CL →

• arXiv
• Claude Sonnet 5.5
• GPT-5.6 Sol
• Medicaid
• U.S.

• paper
• safety

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