134 citations · 776 across the 124 of their papers we have counts for
9 papers · 1 filter
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…
Learning Compact Boolean Networks
Shengpu Wang, Yuhao Mao, Yani Zhang +1
Floating-point neural networks dominate modern machine learning but incur substantial inference costs, motivating emerging interest in Boolean networks for resource-constrained dep…
BrokenMath: A Benchmark for Sycophancy in Theorem Proving with LLMs
Ivo Petrov, Jasper Dekoninck, Martin Vechev
Large language models (LLMs) have recently shown strong performance on mathematical benchmarks. At the same time, they are prone to hallucination and sycophancy, often providing co…
MathArena: Evaluating LLMs on Uncontaminated Math Competitions
Mislav Balunović, Jasper Dekoninck, Ivo Petrov +2
The rapid advancement of reasoning capabilities in large language models (LLMs) has led to notable improvements on mathematical benchmarks. However, many of the most commonly used…
ToolFuzz -- Automated Agent Tool Testing
Ivan Milev, Mislav Balunović, Maximilian Baader +1
Large Language Model (LLM) Agents leverage the advanced reasoning capabilities of LLMs in real-world applications. To interface with an environment, these agents often rely on tool…
MathConstruct: Challenging LLM Reasoning with Constructive Proofs
Mislav Balunović, Jasper Dekoninck, Nikola Jovanović +2
While Large Language Models (LLMs) demonstrate impressive performance in mathematics, existing math benchmarks come with significant limitations. Many focus on problems with fixed…
Private Attribute Inference from Images with Vision-Language Models
Batuhan Tömekçe, Mark Vero, Robin Staab +1
As large language models (LLMs) become ubiquitous in our daily tasks and digital interactions, associated privacy risks are increasingly in focus. While LLM privacy research has pr…
Large Language Models are Advanced Anonymizers
Robin Staab, Mark Vero, Mislav Balunović +1
Recent privacy research on large language models (LLMs) has shown that they achieve near-human-level performance at inferring personal data from online texts. With ever-increasing…
Beyond Memorization: Violating Privacy Via Inference with Large Language Models
Robin Staab, Mark Vero, Mislav Balunović +1
Current privacy research on large language models (LLMs) primarily focuses on the issue of extracting memorized training data. At the same time, models' inference capabilities have…