5 papers
AI Organizations are More Effective but Less Aligned than Individual Agents
Judy Hanwen Shen, Daniel Zhu, Siddarth Srinivasan +5
AI is increasingly deployed in multi-agent systems; however, most research considers only the behavior of individual models. We experimentally show that multi-agent "AI organizatio…
Abstractive Red-Teaming of Language Model Character
Nate Rahn, Allison Qi, Avery Griffin +3
We want language model assistants to conform to a character specification, which asserts how the model should act across diverse user interactions. While models typically follow th…
Inoculation Prompting: Instructing LLMs to misbehave at train-time improves test-time alignment
Nevan Wichers, Aram Ebtekar, Ariana Azarbal +8
Large language models are sometimes trained with imperfect oversight signals, leading to undesired behaviors such as reward hacking and sycophancy. Improving oversight quality can…
Believe It or Not: How Deeply do LLMs Believe Implanted Facts?
Stewart Slocum, Julian Minder, Clément Dumas +4
Knowledge editing techniques promise to implant new factual knowledge into large language models (LLMs). But do LLMs really believe these facts? We develop a framework to measure b…
All Code, No Thought: Current Language Models Struggle to Reason in Ciphered Language
Shiyuan Guo, Henry Sleight, Fabien Roger
Detecting harmful AI actions is important as AI agents gain adoption. Chain-of-thought (CoT) monitoring is one method widely used to detect adversarial attacks and AI misalignment.…