3 papers
cs.LG2026
FLIPS: Instance-Fingerprinting for LLMs via Pseudo-random Sequences
Gurvan Richardeau, Gohar Dashyan, Erwan Le Merrer +1
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…
cs.CL2024
The 20 questions game to distinguish large language models
Gurvan Richardeau, Erwan Le Merrer, Camilla Penzo +1
In a parallel with the 20 questions game, we present a method to determine whether two large language models (LLMs), placed in a black-box context, are the same or not. The goal is…
cs.CL2024
LLMs Prompted for Graphs: Hallucinations and Generative Capabilities
Gurvan Richardeau, Samy Chali, Erwan Le Merrer +2
Large Language Models (LLMs) are nowadays prompted for a wide variety of tasks. In this article, we investigate their ability in reciting and generating graphs. We first study the…