2 citations · 2 across the 4 of their papers we have counts for
4 papers
Conditional misalignment: common interventions can hide emergent misalignment behind contextual triggers
Jan Dubiński, Jan Betley, Anna Sztyber-Betley +2
Finetuning a language model can lead to emergent misalignment (EM) [Betley et al., 2025b]. Models trained on a narrow distribution of misaligned behavior generalize to more egregio…
Me, Myself, and AI: The Situational Awareness Dataset (SAD) for LLMs
Rudolf Laine, Bilal Chughtai, Jan Betley +6
AI assistants such as ChatGPT are trained to respond to users by saying, "I am a large language model". This raises questions. Do such models know that they are LLMs and reliably a…
Can Language Models Explain Their Own Classification Behavior?
Dane Sherburn, Bilal Chughtai, Owain Evans
Large language models (LLMs) perform well at a myriad of tasks, but explaining the processes behind this performance is a challenge. This paper investigates whether LLMs can give f…
Tell, don't show: Declarative facts influence how LLMs generalize
Alexander Meinke, Owain Evans
We examine how large language models (LLMs) generalize from abstract declarative statements in their training data. As an illustration, consider an LLM that is prompted to generate…