72 citations · 108 across the 10 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…
Analyzing Probabilistic Methods for Evaluating Agent Capabilities
Axel Højmark, Govind Pimpale, Arjun Panickssery +2
To mitigate risks from AI systems, we need to assess their capabilities accurately. This is especially difficult in cases where capabilities are only rarely displayed. Phuong et al…
TracrBench: Generating Interpretability Testbeds with Large Language Models
Hannes Thurnherr, Jérémy Scheurer
Achieving a mechanistic understanding of transformer-based language models is an open challenge, especially due to their large number of parameters. Moreover, the lack of ground tr…
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…
Black-Box Access is Insufficient for Rigorous AI Audits
Stephen Casper, Carson Ezell, Charlotte Siegmann +18
External audits of AI systems are increasingly recognized as a key mechanism for AI governance. The effectiveness of an audit, however, depends on the degree of access granted to a…