12 citations · 29 across the 13 of their papers we have counts for
5 papers · 1 filter
Towards evaluations-based safety cases for AI scheming
Mikita Balesni, Marius Hobbhahn, David Lindner +13
We sketch how developers of frontier AI systems could construct a structured rationale -- a 'safety case' -- that an AI system is unlikely to cause catastrophic outcomes through sc…
Transformer Circuit Faithfulness Metrics are not Robust
Joseph Miller, Bilal Chughtai, William Saunders
Mechanistic interpretability work attempts to reverse engineer the learned algorithms present inside neural networks. One focus of this work has been to discover 'circuits' -- subg…
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…
Summing Up the Facts: Additive Mechanisms Behind Factual Recall in LLMs
Bilal Chughtai, Alan Cooney, Neel Nanda
How do transformer-based large language models (LLMs) store and retrieve knowledge? We focus on the most basic form of this task -- factual recall, where the model is tasked with e…