5 papers · 1 filter
Large Language Model Hacking: Quantifying the Hidden Risks of Using LLMs for Text Annotation
Joachim Baumann, Paul Röttger, Aleksandra Urman +4
Large language models are rapidly transforming social science research by enabling the automation of labor-intensive tasks like data annotation and text analysis. However, LLM outp…
A Good Plan is Hard to Find: Aligning Models with Preferences is Misaligned with What Helps Users
Nishant Balepur, Matthew Shu, Yoo Yeon Sung +5
To assist users in complex tasks, LLMs generate plans: step-by-step instructions towards a goal. While alignment methods aim to ensure LLM plans are helpful, they train (RLHF) or e…
Around the World in 24 Hours: Probing LLM Knowledge of Time and Place
Carolin Holtermann, Paul Röttger, Anne Lauscher
Reasoning over time and space is essential for understanding our world. However, the abilities of language models in this area are largely unexplored as previous work has tested th…
Compromesso! Italian Many-Shot Jailbreaks Undermine the Safety of Large Language Models
Fabio Pernisi, Dirk Hovy, Paul Röttger
As diverse linguistic communities and users adopt large language models (LLMs), assessing their safety across languages becomes critical. Despite ongoing efforts to make LLMs safe,…
PERSONA: A Reproducible Testbed for Pluralistic Alignment
Louis Castricato, Nathan Lile, Rafael Rafailov +2
The rapid advancement of language models (LMs) necessitates robust alignment with diverse user values. However, current preference optimization approaches often fail to capture the…