132 citations · 273 across the 15 of their papers we have counts for
6 papers · 1 filter
Spilling the Beans: Teaching LLMs to Self-Report Their Hidden Objectives
Chloe Li, Mary Phuong, Daniel Tan
As AI systems become more capable of complex agentic tasks, they also become more capable of pursuing undesirable objectives and causing harm. Previous work has attempted to catch…
LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring
Chloe Li, Mary Phuong, Noah Y. Siegel
Trustworthy evaluations of dangerous capabilities are increasingly crucial for determining whether an AI system is safe to deploy. One empirically demonstrated threat is sandbaggin…
Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety
Tomek Korbak, Mikita Balesni, Elizabeth Barnes +38
AI systems that "think" in human language offer a unique opportunity for AI safety: we can monitor their chains of thought (CoT) for the intent to misbehave. Like all other known A…
CoT Red-Handed: Stress Testing Chain-of-Thought Monitoring
Benjamin Arnav, Pablo Bernabeu-Pérez, Nathan Helm-Burger +3
As AI models are deployed with increasing autonomy, it is important to ensure they do not take harmful actions unnoticed. As a potential mitigation, we investigate Chain-of-Thought…
Evaluating Frontier Models for Stealth and Situational Awareness
Mary Phuong, Roland S. Zimmermann, Ziyue Wang +6
Recent work has demonstrated the plausibility of frontier AI models scheming -- knowingly and covertly pursuing an objective misaligned with its developer's intentions. Such behavi…
From Stability to Inconsistency: A Study of Moral Preferences in LLMs
Monika Jotautaite, Mary Phuong, Chatrik Singh Mangat +1
As large language models (LLMs) increasingly integrate into our daily lives, it becomes crucial to understand their implicit biases and moral tendencies. To address this, we introd…