New method verifies LLM API model authenticity statistically

A method has been developed to detect if an API serving open-weight language models is substituting a cheaper or smaller model than advertised. The intuitive approach of grading output quality proved ineffective, as simpler, more predictable text from a cheaper model was paradoxically scored higher. A more reliable method involves asking the model to score a fixed token sequence, where a genuine model will assign a higher log-probability to text it would produce. This statistical check requires accumulating evidence over multiple challenges, as a single test is insufficient to distinguish between different model quantizations or slight downgrades. AI

IMPACT Enables users to statistically verify that open-weight LLM APIs are serving the advertised model, preventing deceptive substitutions.

RANK_REASON The item details a novel technical method for verifying the authenticity of served language models, which is a form of research into model integrity and infrastructure. [lever_c_demoted from research: ic=1 ai=0.7]

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