9 papers
Measuring and Mitigating Persona Distortions from AI Writing Assistance
Paul Röttger, Kobi Hackenburg, Hannah Rose Kirk +1
Hundreds of millions of people use artificial intelligence (AI) for writing assistance. Here, we evaluated how AI writing assistance distorts writer personas - their perceived beli…
Diffusion Language Models Are Natively Length-Aware
Vittorio Rossi, Giacomo Cirò, Davide Beltrame +3
Unlike autoregressive language models, which terminate variable-length generation upon predicting an End-of-Sequence (EoS) token, Diffusion Language Models (DLMs) operate over a fi…
TrojanStego: Your Language Model Can Secretly Be A Steganographic Privacy Leaking Agent
Dominik Meier, Jan Philip Wahle, Paul Röttger +2
As large language models (LLMs) become integrated into sensitive workflows, concerns grow over their potential to leak confidential information. We propose TrojanStego, a novel thr…
IssueBench: Millions of Realistic Prompts for Measuring Issue Bias in LLM Writing Assistance
Paul Röttger, Musashi Hinck, Valentin Hofmann +4
Large language models (LLMs) are helping millions of users write texts about diverse issues, and in doing so expose users to different ideas and perspectives. This creates concerns…
No for Some, Yes for Others: Persona Prompts and Other Sources of False Refusal in Language Models
Flor Miriam Plaza-del-Arco, Paul Röttger, Nino Scherrer +3
Large language models (LLMs) are increasingly integrated into our daily lives and personalized. However, LLM personalization might also increase unintended side effects. Recent wor…
The Pluralistic Moral Gap: Understanding Judgment and Value Differences between Humans and Large Language Models
Giuseppe Russo, Debora Nozza, Paul Röttger +1
People increasingly rely on Large Language Models (LLMs) for moral advice, which may influence humans' decisions. Yet, little is known about how closely LLMs align with human moral…