AI Research
SetPrint: Black-Box LLM Fingerprinting via Setwise Correspondence of Response Changes
A black-box approach examines response changes to investigate model lineage.
Black-box LLM fingerprinting aims to determine whether a target model was derived from a source model when only the output responses to input queries are observable.
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ArXiv CS.AI
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ArXiv CS.AI
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Black-box fingerprinting could help assess whether a model was derived from another even when its internals are unavailable. Setwise comparison of response changes offers a way to frame that investigation using observable outputs.
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