FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences 事件

FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences arXiv:2606.03330v1 Announce Type: cross Abstract: Literature reveals that a Large Language Model's (LLM) behavior is not only conditioned by its original weights but also its instance-level parameters, such as instructional prompt, sampling configuration or quantization. A model that generates safe outputs under one configuration may produce toxic content under another. However, current LLM identification techniques (such as fi