7 papers
Muse Spark Safety & Preparedness Report
Cristina Menghini, Peter Ney, Hamza Kwisaba +117
Muse Spark is the latest large language model developed by Meta. In this report, we first present evaluations for catastrophic risk domains under Meta's Advanced AI Scaling Framewo…
HorizonBench: Long-Horizon Personalization with Evolving Preferences
Shuyue Stella Li, Bhargavi Paranjape, Kerem Oktar +9
User preferences evolve across months of interaction, and tracking them requires inferring when a stated preference has been changed by a subsequent life event. We define this prob…
Under the Influence: Quantifying Persuasion and Vigilance in Large Language Models
Sasha Robinson, Katherine M. Collins, Ilia Sucholutsky +1
With increasing integration of Large Language Models (LLMs) into areas of high-stakes human decision-making, it is important to understand the risks they introduce as advisors. To…
When Large Language Models are More PersuasiveThan Incentivized Humans, and Why
Philipp Schoenegger, Francesco Salvi, Jiacheng Liu +39
Large Language Models (LLMs) have been shown to be highly persuasive, but when and why they outperform humans is still an open question. We compare the persuasiveness of two LLMs (…
Are Large Language Models Sensitive to the Motives Behind Communication?
Addison J. Wu, Ryan Liu, Kerem Oktar +2
Human communication is motivated: people speak, write, and create content with a particular communicative intent in mind. As a result, information that large language models (LLMs)…
Identifying, Evaluating, and Mitigating Risks of AI Thought Partnerships
Kerem Oktar, Katherine M. Collins, Jose Hernandez-Orallo +4
Artificial Intelligence (AI) systems have historically been used as tools that execute narrowly defined tasks. Yet recent advances in AI have unlocked possibilities for a new class…