collaborators

5 papers

cs.CL2026

How I Aligned Your Model: Scaling Up Multilingual Safety Training with Templates

Max Neuwinger, Hanna Yukhymenko, Imanol Schlag

Safety alignment for Large Language Models is heavily focused on English with limited multilingual representation, particularly damaging low-resource language performance. Related…

cs.CR2026

Fetch Me If You Can: A Comprehensive Analysis of Fetch Metadata

Pedro Bernardo, Alex Ploner, Marco Squarcina +2

Effective server-side defenses against cross-site attacks depend on understanding the context in which browser requests are generated. However, servers often lack the information n…

cs.CL2026

Last Translation Benchmark

Vilém Zouhar, Niyati Bafna, Mukund Choudhary +263

For scientific progress, we need benchmarks that test the limits of state-of-the-art models, and evaluation methods that inform us about failure cases. As models get stronger, stan…

cs.CL2026

Recovered in Translation: Efficient Pipeline for Automated Translation of Benchmarks and Datasets

Hanna Yukhymenko, Anton Alexandrov, Martin Vechev

The reliability of multilingual Large Language Model (LLM) evaluation is currently compromised by the inconsistent quality of translated benchmarks. Existing resources often suffer…

cs.LG2024

A Synthetic Dataset for Personal Attribute Inference

Hanna Yukhymenko, Robin Staab, Mark Vero +1

Recently, powerful Large Language Models (LLMs) have become easily accessible to hundreds of millions of users world-wide. However, their strong capabilities and vast world knowled…