3 citations · 4 across the 9 of their papers we have counts for
5 papers · 1 filter
Guided Diffusion for Trustworthy Synthetic Text Generation
Qi Pang, Hanshen Xiao, Andre Manoel +4
The rapid evolution of Large Language Models is fueled by massive datasets, yet this reliance poses severe privacy risks, as models can inadvertently memorize and even directly out…
Codetta: High-Capacity, Keyless, and Undetectable Multi-Agent Collusion
Qi Pang, Virginia Smith, Wenting Zheng
Multi-agent systems built on large language models (LLMs) are increasingly deployed in high-stakes settings such as finance, healthcare, and software engineering, where agents coor…
Safety Nudges: User-Facing Interventions for Real-Time AI Risk Awareness
Varshini Elangovan, James Wedgwood, Chhavi Yadav +3
Conversational AI systems can pose safety risks to their users such as hallucination, sycophancy, overconfidence, and anthropomorphism, but these risks are difficult for users to d…
Personalization, Personas, and Forecasting in Value Alignment
James Wedgwood, Pratiksha Thaker, Neil Kale +1
LLM behavior may be conditioned by human identity in several ways: they may be asked to adapt to users, role-play populations, or forecast how people would answer value-laden quest…
Position: Preventing AI-Generated CSAM Necessitates New Approaches to AI Safety
Neil Kale, Rebecca Portnoff, Pratiksha Thaker +5
Modern artificial intelligence (AI) systems present profound new risks to child safety. AI is increasingly being misused to create AI-generated child sexual abuse material, facilit…