20 papers
Adaptive Generation of Bias-Eliciting Questions for LLMs
Robin Staab, Jasper Dekoninck, Maximilian Baader +1
Large language models (LLMs) are now widely deployed in user-facing applications, reaching hundreds of millions of users worldwide. Despite their widespread adoption, growing relia…
Widening the Gap: Exploiting LLM Quantization via Outlier Injection
Xiaohua Zhan, Kazuki Egashira, Robin Staab +2
LLM quantization has become essential for memory-efficient deployment. Recent work has shown that quantization schemes can pose critical security risks: an adversary may release a…
Marking the Wrong Symptoms: Evaluating LLM Watermarks in Medical Texts
Melanie Rieff, Robin Staab, Thibaud Gloaguen +2
Large language models (LLMs) are increasingly integrated into clinical workflows, stressing the need for reliable traceability of model-generated output with watermarking. Yet, mos…
Every Bit, Everywhere, All at Once: A Binomial Multibit LLM Watermark
Thibaud Gloaguen, Robin Staab, Mark Vero +1
With LLM watermarking already being deployed commercially, practical applications increasingly require multibit watermarks that encode more complex payloads, such as user IDs or ti…
Making Open-Source Text LLM Watermarks Durable Against Merging
Luisa Scharff, Thibaud Gloaguen, Robin Staab +1
Open-source LLMs (OSMs)arereaching near state-of-the-art performance, prompting prior works to trace the text they generate by embedding text watermarking algorithms directly into…
Delay, Plateau, or Collapse: Evaluating the Impact of Systematic Verification Error on RLVR
Kazuki Egashira, Mark Vero, Jasper Dekoninck +3
Reinforcement Learning with Verifiable Rewards (RLVR) has become a powerful approach for improving the reasoning capabilities of large language models (LLMs). While RLVR is designe…