1.8k citations · 2.9k across the 27 of their papers we have counts for
30 papers
Deception by Omission: Language Models Knowingly Hide Their Mistakes
Lucas Florin, Amelie Knecht, Ulysse Schaller +1
Large language models (LLMs) increasingly act as agents with little human oversight, so potential mistakes they make can go unnoticed. Users then depend on the model to report what…
How Much Do LLM-as-a-Judge Design Choices Matter? A Systematic Comparison of Prompt Designs, Rating Scales, and Models
Laurène Vaugrante, Thilo Hagendorff
Researchers increasingly use Large Language Models as judges (LLM-as-a-judge) to evaluate model outputs. Yet there are no standards for how to design these judges. Typically, resea…
Shutdown Sabotage Propensities in Multi-Agent Systems
Amelie Knecht, Ulysse Schaller, Christopher Summerfield +1
The final safeguard against rogue AI behavior is the human ability to shut systems down. It has been theorized that when an AI is instructed to perform a task, self-preservation ca…
Evaluation Awareness in Language Models Has Limited Effect on Behaviour
Amelie Knecht, Lucas Florin, Thilo Hagendorff
Large reasoning models (LRMs) sometimes note in their chain of thought (CoT) that they may be under evaluation. Researchers worry that this verbalised evaluation awareness (VEA) ca…
"Dark Triad" Model Organisms of Misalignment: Narrow Fine-Tuning Mirrors Human Antisocial Behavior
Roshni Lulla, Fiona Collins, Sanaya Parekh +2
The alignment problem refers to concerns regarding powerful intelligences, ensuring compatibility with human preferences and values as capabilities increase. Current large language…
Emergently Misaligned Language Models Show Behavioral Self-Awareness That Shifts With Subsequent Realignment
Laurène Vaugrante, Anietta Weckauff, Thilo Hagendorff
Recent research has demonstrated that large language models (LLMs) fine-tuned on incorrect trivia question-answer pairs exhibit toxicity - a phenomenon later termed "emergent misal…